Proceeding OF International Conference on Science and Technology 2K14 (ICST-2K14)
|
|
- Anabel Harrison
- 6 years ago
- Views:
Transcription
1 PREDICTION OF DAILY RUNOFF USING TIME SERIES FORECASTING AND ANN MODELS Santosh K Patil 1, Dr. Shrinivas S. Valunjkar Research scholar Dept. of Civil Engineering, Government College of Engineering, Aurangabad, India Professor, Dept. of Civil Engineering, Government College of Engineering, Karad, Satara, India santosh68.patil@gmail.com 1, ssvalunjkar@gmail.com ABSTRACT Today s scenario motivates the researchers to develop innovative models for the need of increased accuracies in time series forecasting. This paper proposes a new time series neural network model that utilizes the strengths of traditional time series approaches and artificial neural networks (ANN s). The proposed approach towards overall modeling framework is a combination of the conventional and ANN techniques. Before presenting the modified time series data to the ANN, data processing is implicated in the time series analysis. In this paper, the daily stream flow data of Gunjwani River near Pune, Maharashtra, India is tested for time series forecasting. The results from time series models of ARIMA and ANN models are presented. More accurate forecasts can be produced due to the approach of combining the strengths of the conventional and ANN techniques and is confirmed by the results obtained that provide a robust modeling framework capable of capturing the non-linear nature of the complex time series and hence producing more accurate forecasts. In this study, the proposed neural network models are applied in hydrology, but they also have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting. Keyword Time series, ANN techniques, daily stream flow, Hydrology Introduction In the last few decades, time series forecasting has received tremendous attention of researchers. Planning, designing, management and other important activities in all branches of engineering and other fields need the time series forecasting methods. Conventionally, the researchers have employed traditional methods of time series analysis, modeling, and forecasting, e.g. Box-Jenkins methods of autoregressive (AR), auto-regressive moving average (ARMA), auto-regressive integrated moving average (ARIMA), auto-regressive moving average with exogenous inputs (ARMAX), etc. The conventional time series modeling methods have served the scientific community for a long time; though, they provide only reasonable accuracy and suffer from the stationary and linear assumptions. About two decades ago artificial neural networks (ANNs) were introduced as efficient tools of modeling and forecasting. One can find numerous ANN applications in a wide range of areas for time series forecasting. A great deal of time and effort has been spent by the researchers in both conventional and soft computing techniques for time series forecasting. However, the need of producing more and more accurate time series forecasts has forced the researchers to develop innovative methods to model time series. This paper presents a study aimed at achieving accurate forecasts of a hydrologic time series using a combination of traditional time series and neural network approaches. The paper begins with a brief review of the time series forecasting using neural networks in a wide range of fields. Hydrologic time series modeling The stream flow at a location in a river in a catchment is one of the key hydrologic variables. The availability of accurate stream flow forecasts at a location in a river in a catchment is important in many water resources management and design activities such as flood control and management and design of various hydraulic structures such as dams and bridges. To generate stream flow forecasts two types of mathematical models are used namely the rainfall-runoff models that use both climatic and hydrologic data and stream flow models that use only the hydrologic data. The researchers, usually, have relied on conventional modeling techniques, either deterministic or conceptual models that consider the physics of the underlying process or systems theoretic/ black box models that do not. Deterministic and black-box models of varying degree of complexity have been employed in the past for modeling rainfall-runoff process with a varying degree of success. The stream flow process in a catchment is a complex and non-linear processes affected by many and often inter related physical factors. The factors affecting the stream flow response of a catchment subjected to rainfall input include: (a) storm characteristics, i.e. intensity and duration of rainfall event,(b) catchment characteristics, i.e. size, shape, slope and storage characteristics of the catchment, percentage of the catchment contributing stream flow at the outlet at various time steps during a rainfall event, (c) geomorphological characteristics of a catchment, i.e. topography, 1
2 land use pattern, soil type, vegetation that affects infiltration and (d) climatic characteristics such as temperature, humidity and wind characteristics. The influence of these factors and many of their combinations in generating stream flow is an extremely complex physical process and is not understood clearly [1]. Moreover, many of the deterministic or conceptual rainfall-runoff models need a large amount of data for calibration and validation purposes and are computationally extensive. As a result, the use of deterministic/ conceptual models of the rainfall-runoff process has been viewed rather skeptically by the researchers and has now become very popular []. ANNs have been proposed as efficient tools for modeling and prediction and are supposed to possess the capability to reproduce the unknown relationship existing between a set of input explanatory variables and output variables [3]. Many studies have demonstrated that the ANNs are adequate to model the runoff process and can even perform better than the conventional modeling technique [1, -1]. Many efforts has been spent in using traditional time series analysis techniques for stream flow forecasting. Many models of varying degree of complexity and sophistication have been proposed by various researchers. Some of the earliest examples of the AR type of stream flow forecast models include Thomas and Fiering [11]. Carlson et al. []proposed significant developments in the form of ARMA models of the hydrologic time series. McKerchar and Delleur [13] used the ARIMA modeling to model monthly stream flow of 16 watersheds in Indiana, Illinois and Kentucky. An important contribution to the time series modeling was due to Kalman [1], in the form of model capability to operate in an adaptive sense. He provided a mechanism, which would minimize the forecast error variance. Some other notable examples of the use of time series modeling for stream flow forecasting include Bolzern et al. [15], Lettenmaier [16], Burn and McBean [17], Bender and Simonivic [18]and Awwad et al.[19]. The ANN modeling of stream flow using only flow data and the comparison of ANN models with the time series models have been limited in hydrology. Atiya et al. []compared time series and ANN models for making single-step and multiple-step ahead forecasts for river flow. Jain et al. [1]compared the ANN models with regression and time series models in making short term water demand predictions at the Indian Institute of Technology, Kanpur. Jain and Ormsbee []used ANN to model the short-term water demand process in U SA, and found its performance to be better than the regression and time series models of AR type. Jain and Indurthy [3]used past flow information to model the complex rainfallrunoff process and compared the same with regression models. Coulibaly et al. [] presented multilayer perceptron (MLP), input delayed neural network and recurrent neural network with and without input delays for reservoir inflow prediction in the Chute-du-Diable catchment in Canada. Apart from these studies, the efforts in the area of using ANNs for time series modeling and prediction in hydrology have been limited. W hile developing ANN models of the hydrologic time series, most of the researchers have employed raw data to be presented to the ANN. The raw data consist of various trends in the form of long term memory and seasonal variations. For these reasons, the hydraulic time series may be non-stationary affecting the performance of the ANN models. It may be possible to improve the performance of ANN models by first carefully removing the long-term variations before presenting an ANN with the modified data. The conventional time series modeling approaches of ARMA type suffer from being based on the linear systems theory. The non-linear and massively parallel structure of ANNs coupled with traditional time series methods may provide robust modeling framework capable of producing more accurate forecasts; however, it needs to be investigated. The objectives of the study presented in this paper are to: (a) investigate the use of non-linear ANNs for modeling the complex hydrologic time series, (b) evaluate the impact of removing the long-term and seasonal variations in a time series before presenting the filtered data to an ANN on prediction and modeling and (c) compare the performance of the proposed approach with the traditional time series models. All the models and the proposed methodologies are tested using the daily stream flow data. The development of various models is presented next. Model Development Two types of approaches have been investigated in this study for the purpose of stream flow forecasting. The first approach involves the conventional time series modeling of AR type and the second one uses ANN approach. In addition, each of the two approaches is tested on two categories of data: raw data consisting of daily flow and the de-trended de-seasonalised data after removing long-term trends and seasonal variations. The purpose of using de-trended de-seasonalised data was to evaluate the impact of de-trending and de-seasonalisation of time series modeling on the performance of ANN models. The ANN models developed on data in second category represents hybrid models. Daily stream flow seasonal (June-Oct) data for a period of 3 years (1985-7) derived from the Gunjwani River at velhe near pune, were employed for the model development in this study. The stream flow data for the first 16 years (1985-) were employed for training and the data for the remaining 7 years were employed for testing purpose.
3 Auto-regressive models The steps involved in developing a time series model of AR type include modeling of long-term trends, modeling of seasonal variations and modeling of the auto-correlation structure of the time series. The long term trends were removed by subtracting the annual average stream flow from the original time series to obtain the de-trended time series. The seasonal variations can be modeled using the arithmetic mean approach for its smoothness and superiority in modeling the seasonal effects and to determine the de-trended de-seasonalised time series. The data were normalized to have a mean of. and a variance of 1. before exploring for the auto-correlation structure. A simple normalization expression was employed for this purpose: developed in this study consisted of three layers: an input layer consisting of input explanatory variables, one hidden layer and an output layer consisting of a single neuron representing the flow to be modeled at time t. Four different ANN models were developed for each category of data set. The first ANN model (ANN M1) consisted of the past stream flow Q (t-1) as the input, the second ANN model (ANN M) consisted of the past days stream flow Q (t-1) and Q (t-) as the input, the third ANN model (ANN M3) was formed by the input vector consisting of the past 3 days stream flow Q (t-1), Q (t-) and Q (t-3) as the input. The number of neurons in hidden layer was determined by using trial and error procedure for each model. The data were scaled in the range of and 1. The TanhAxon activation function and Levenberg Marquart learning rule were employed in all the ANN models developed in this study. where, is the normalized time series variable, the original time series variable, the mean of original time series data, is the standard deviation of the original time series data. From the auto-correlation and partial auto-correlation analysis by trial and error method an auto-regressive models are tried and presented in the study. The structure of the AR models can be represented by the following equation: Performance evaluation criteria Two different types of standard statistical performance evaluation criteria were employed to evaluate the performance of various models developed in this study. Mean square error (MSE) is selected as a measure for indicating goodness-of-fit at high output values. Correlation coefficient (R) is a popular global error statistics for measuring goodness-of-fit of the models and trends to give higher weightage to the high magnitude runoff due to square of the difference between observed and predicted stream flows. where Q (t) is the daily stream flow being modeled, Q (t-1) the past stream flow, is the auto-regressive parameters to be determined, the order of the auto-regressive process, an index representing the order of the AR process, a random variable and is an index representing time. In developing the AR models for category 1, the auto-correlation step was carried out on the raw data and for AR models in the data category, the auto correlation model was developed using de-trended and de-seasonalised data. This was done in an attempt to compare the performance of the AR models with the corresponding ANN models. Once the estimates of the AR coefficients have been obtained using the training data set, the model can be validated by computing the performance statistics during both training and testing ANN models The feed-forward multilayer perceptron type ANN model architecture was considered in this study to develop time series models of the non-linear type in an attempt to improve the performance of stream flow forecasting. The ANN models. Results and Discussion The results in terms of various performance evaluation measures are presented in Table.1 and Table. for the two categories of data, respectively. It can be noted from Table.1 that the performance of the three AR models in terms of all the statistics is poor. On the other hand, the performance of the ANN models (ANN M1-ANN M3), significantly better than the corresponding AR models. This is highlighted by the best R value of.91 from the ANN M model. The performance of AR(3) model was the best among AR models and the performance of the ANN model was the best among all the models of category 1, i.e. for which the original data was used for model development. Also, all ANN models consistently outperformed the AR models. Analysing the results from Table. when the de-trended 3
4 de-seasonalised data are employed for model development, it can be observed that the trends in the model performances are similar, i.e. the performance improves with an increase in the order of the AR process. The performance of the AR models was improved slightly but not considerably because data employed in the present study does not require trend analysis and also only seasonal data was employed. However, the ANN models still consistently outperformed the AR models. The AR (3) model performed the best among the AR models and obtained the best values of MSE and R of 7 and.797. The ANN M model obtained best values of MSE and R of 1 and.9. For being employed in important water resources management applications for use in daily stream flow forecasting, it is desirable to have a model that is robust (measured by R value). 1 Qt (Predicted) 8 6 Table.1:Performance evaluation statistics for data category 1 models Model MSE R AR (1) AR () AR (3) ANN M ANN M 3.91 ANN M Table. Performance evaluation statistics for data category models Model MSE R AR (1) AR () AR (3) ANN M1.9 ANN M 1.9 ANN M Scatter Plot of AR (3) Qt (observed) Figure 1: Scatter Plot of AR(3) Predicted stream flow Qt (Predicted) Predicted stream flow Conclusions Time Series Plot of AR (3) time in days Figure : Time Series Plot of AR(3) Scatter Plot of ANN Qt (observed) Figure 3: Scatter Plot of ANN M 15 1 Time Series Plot of ANN time in days Figure : Time Series Plot of ANN M This study presents the findings of an investigation of the use of ANNs and traditional time series approaches for achieving improved accuracies in time series forecasting. A proposed new approach of modeling complex time series is capable of exploiting the advantages of both the conventional and the ANNs. For the proposed model development, the data was filtered using conventional method and then used as input to the ANNs. The daily stream flow data were employed to develop all models and tested the proposed methodology. The results obtained in this study indicate that, the ANNs are powerful tools to model the complex time series and need to be exploited further. The ANNs were able to capture the hidden relationship among the historical stream flows and the future flows in a much better manner than the conventional time series models. The ANN models were able to produce more accurate forecasts. The findings of this study have revealed
5 that using mathematical filters to filter out the long-term and seasonal variations in the data before presenting data to the ANNs can be extremely useful in producing more accurate time series forecasts. References 1. B. Zhang, S. Govindaraju, Prediction of watershed runoff using Bayesian concepts and modular neural networks, W ater Resour. Res. 36(3) (), pp R. B. Grayson, I. D. Moore, T. A. McMahon, Physically based hydrologic modeling, W ater Resour. Res. 8(1) (199), pp K. Chakraborty, K. Mehrotra, C. K. Mohan, S. Ranka, Forecasting the behavior of the multivariate time series using neural networks, Neural Networks 5 (199), pp M. I. Zhu, M. Fujita, N. Hashimoto, Application of neural networks to runoff predictions, Stochastic and Statistical Methods in Hydrology and Environmental Engineering, Kluwer Academic, Norwell, MA, 199, pp J. Smith, R. N. Eli, Neural network models of the rainfall runoff process, J. W ater Resour. Plan, Manage. ASCE 1 (1995) pp A. W. Minns, M. J. Hall, Artificial neural networks as rainfall runoff models, Hydrol. Sci. J. 1 (3) (1996), pp A.Y. Shamseldin, Application of neural network technique to rainfall-runoff modeling, J. Hydrol. 199 (1997), pp D. W. Dawson, R. W ilby, An artificial neural network approach to rainfall-runoff modeling, Hydrol. Sci. J. 3 (1) (1998), pp MM. Campolo, A. Soldati, P. Andreussi, Forecasting river flow rate during low-flow periods using neural networks, W ater Resour. Res. 35 (11) (1999), pp A. Jain, S. Srinivasulu, Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms, and artificial neur.al network techniques, W ater Resour. Res. () () 11. H. A. Thomas, M. B. Fiering, Mathematical synthesis of stream flow sequences for the analysis of river basin by simulation, Design of W ater Resources System, Harvard U niversity press, Cambridge, (196), pp R. F. Carlson, A.I.A. MacCormick, D. G. W atts, Application of linear models to four annual stream flow series, W ater Resour. Res. 6 () (197), pp Chat 13. A. I. Mckercher, J. W. Delleur, Application of seasonal parametric stochastic models for monthly flow data, W ater Resour. Res. (197), pp R. E. Kalman, A new approach to linear filtering and prediction problems, ASME Trans. Basic Eng. 8 () (196), pp P. M. Bolzern, G. Ferrario, A daptve real time forecast of river flow rates from rainfall data, J. Hydrol. 7 (198), pp D. P. Lettenmaier, Synthetic stream flow forecast generation, J. Hydraul. Eng. ASCE 11 (3) (198), pp D. H. Burn, E. A. McBean, River flow forecasting model for Sturgeon River, J. Hydrul. Eng. ASCE 118 (6) (199), pp M. Bender, S. Simovinic, Time series modeling for longrange stream flow fore Han casting, J. W ater Resour. Plan. Manage. ASCE 118 (6) (199), pp H. Awwad, J. Valdes, P. Restrepo, Stream flow forecasting for Han River basin Korea, J. W ater Resour. Plan. Manage. ASCE (5) (199), pp A. F. Atiya, S. M. EI-Shoura, S. I. Shaheen, M. S. EI- Sherif, A comparison between neural network forecasting techniques-case study: river flow forecasting, IEEE Trans. Neural Networks 1 () (1999), pp A. Jain, A. K. Varshney, U. C. Joshi, Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks, W ater Resour. Manage. 15 (5) (1), pp A. Jain, L. E. Ormsbee, Evalution of short-term water demand forecast modeling techniques: conventional v/s artificial intelligence, J. Am. W ater W orks Assoc. 9 (7) (), pp
River Flow Forecasting with ANN
River Flow Forecasting with ANN OMID BOZORG HADDAD, FARID SHARIFI, SAEED ALIMOHAMMADI Department of Civil Engineering Iran University of Science & Technology, Shahid Abbaspour University Narmak, Tehran,
More informationFlood Forecasting Using Artificial Neural Networks in Black-Box and Conceptual Rainfall-Runoff Modelling
Flood Forecasting Using Artificial Neural Networks in Black-Box and Conceptual Rainfall-Runoff Modelling Elena Toth and Armando Brath DISTART, University of Bologna, Italy (elena.toth@mail.ing.unibo.it)
More informationJournal of Urban and Environmental Engineering, v.3, n.1 (2009) 1 6 ISSN doi: /juee.2009.v3n
J U E E Journal of Urban and Environmental Engineering, v.3, n.1 (2009) 1 6 ISSN 1982-3932 doi: 10.4090/juee.2009.v3n1.001006 Journal of Urban and Environmental Engineering www.journal-uee.org USING ARTIFICIAL
More informationPrediction of Monthly Rainfall of Nainital Region using Artificial Neural Network (ANN) and Support Vector Machine (SVM)
Vol- Issue-3 25 Prediction of ly of Nainital Region using Artificial Neural Network (ANN) and Support Vector Machine (SVM) Deepa Bisht*, Mahesh C Joshi*, Ashish Mehta** *Department of Mathematics **Department
More informationForecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models
Journal of Computer Science 2 (10): 775-780, 2006 ISSN 1549-3644 2006 Science Publications Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models
More informationComparison of Regression, ARIMA and ANN Models for Reservoir Inflow Forecasting using Snowmelt Equivalent (a Case study of Karaj)
J. Agric. Sci. Technol. (25) Vol. 7: 17-3 Comparison of Regression, ARIMA and ANN Models for Reservoir Inflow Forecasting using Snowmelt Equivalent (a Case study of Karaj) K. Mohammadi 1*, H. R. Eslami
More informationChapter-1 Introduction
Modeling of rainfall variability and drought assessment in Sabarmati basin, Gujarat, India Chapter-1 Introduction 1.1 General Many researchers had studied variability of rainfall at spatial as well as
More informationMONTHLY RESERVOIR INFLOW FORECASTING IN THAILAND: A COMPARISON OF ANN-BASED AND HISTORICAL ANALOUGE-BASED METHODS
Annual Journal of Hydraulic Engineering, JSCE, Vol.6, 5, February MONTHLY RESERVOIR INFLOW FORECASTING IN THAILAND: A COMPARISON OF ANN-BASED AND HISTORICAL ANALOUGE-BASED METHODS Somchit AMNATSAN, Yoshihiko
More informationForecasting Drought in Tel River Basin using Feed-forward Recursive Neural Network
2012 International Conference on Environmental, Biomedical and Biotechnology IPCBEE vol.41 (2012) (2012) IACSIT Press, Singapore Forecasting Drought in Tel River Basin using Feed-forward Recursive Neural
More informationOptimal Artificial Neural Network Modeling of Sedimentation yield and Runoff in high flow season of Indus River at Besham Qila for Terbela Dam
Optimal Artificial Neural Network Modeling of Sedimentation yield and Runoff in high flow season of Indus River at Besham Qila for Terbela Dam Akif Rahim 1, Amina Akif 2 1 Ph.D Scholar in Center of integrated
More informationInflow forecasting for lakes using Artificial Neural Networks
Flood Recovery Innovation and Response III 143 Inflow forecasting for lakes using Artificial Neural Networks R. K. Suryawanshi 1, S. S. Gedam 1 & R. N. Sankhua 2 1 CSRE, IIT Bombay, Mumbai, India 2 National
More informationAN OVERVIEW OF ENSEMBLE STREAMFLOW PREDICTION STUDIES IN KOREA
AN OVERVIEW OF ENSEMBLE STREAMFLOW PREDICTION STUDIES IN KOREA DAE-IL JEONG, YOUNG-OH KIM School of Civil, Urban & Geosystems Engineering, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul,
More informationEstimation of Pan Evaporation Using Artificial Neural Networks A Case Study
International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 9 (2017) pp. 3052-3065 Journal homepage: http://www.ijcmas.com Case Study https://doi.org/10.20546/ijcmas.2017.609.376
More informationA "consensus" real-time river flow forecasting model for the Blue Nile River
82 Water Resources Systems--Hydrological Risk, Management and Development (Proceedings of symposium HS02b held during IUGG2003 al Sapporo. July 2003). IAHS Publ. no. 281. 2003. A "consensus" real-time
More informationJournal of of Computer Applications Research Research and Development and Development (JCARD), ISSN (Print), ISSN
JCARD Journal of of Computer Applications Research Research and Development and Development (JCARD), ISSN 2248-9304(Print), ISSN 2248-9312 (JCARD),(Online) ISSN 2248-9304(Print), Volume 1, Number ISSN
More informationA time delay artificial neural network approach for flow routing in a river system
Hydrol. Earth Syst. Sci. Discuss., 3, 273 276, 6 www.hydrol-earth-syst-sci-discuss.net/3/273/6/ Author(s) 6. This work is licensed under a Creative Commons License. Hydrology and Earth System Sciences
More informationDaily Reservoir Inflow Forecasting Using Time Delay Artificial Neural Network Models
Daily Reservoir Inflow Forecasting Using Time Delay Artificial Neural Network Models M. J. DIAMANTOPOULOU *, P.E. GEORGIOU ** & D.M. PAPAMICHAIL ** * Faculty of Forestry and Natural Environment, Aristotle
More informationApplication of an artificial neural network to typhoon rainfall forecasting
HYDROLOGICAL PROCESSES Hydrol. Process. 19, 182 1837 () Published online 23 February in Wiley InterScience (www.interscience.wiley.com). DOI: 1.12/hyp.638 Application of an artificial neural network to
More informationStochastic Hydrology. a) Data Mining for Evolution of Association Rules for Droughts and Floods in India using Climate Inputs
Stochastic Hydrology a) Data Mining for Evolution of Association Rules for Droughts and Floods in India using Climate Inputs An accurate prediction of extreme rainfall events can significantly aid in policy
More informationFlash-flood forecasting by means of neural networks and nearest neighbour approach a comparative study
Author(s 2006. This work is licensed under a Creative Commons License. Nonlinear Processes in Geophysics Flash-flood forecasting by means of neural networks and nearest neighbour approach a comparative
More informationINTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 6, No 2, 2015
INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 6, No 2, 2015 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN 0976 4399 Rainfall analysis
More informationUsing Empirical Relationships and Neural Network in GIS for Developing Rainfall-Runoff Model
Using Empirical Relationships and Neural Network in GIS for Developing Rainfall-Runoff Model Shahrbanu FIRUZI, Mohamadbagher SHARIFI, Kambiz BORNA and MohamadReza RAJABI, Iran Key words: Run-off, Artificial
More informationBiological Forum An International Journal 7(1): (2015) ISSN No. (Print): ISSN No. (Online):
Biological Forum An International Journal 7(1): 1205-1210(2015) ISSN No. (Print): 0975-1130 ISSN No. (Online): 2249-3239 Forecasting Monthly and Annual Flow Rate of Jarrahi River using Stochastic Model
More informationRainfall Prediction using Back-Propagation Feed Forward Network
Rainfall Prediction using Back-Propagation Feed Forward Network Ankit Chaturvedi Department of CSE DITMR (Faridabad) MDU Rohtak (hry). ABSTRACT Back propagation is most widely used in neural network projects
More informationAPPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR FLOOD FORECASTING
Global Nest: the Int. J. Vol 6, No 3, pp 4-1, 4 Copyright 4 GLOBAL NEST Printed in Greece. All rights reserved APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR FLOOD FORECASTING D.F. LEKKAS 1,* 1 Department
More informationReal Time wave forecasting using artificial neural network with varying input parameter
82 Indian Journal of Geo-Marine SciencesINDIAN J MAR SCI VOL. 43(1), JANUARY 2014 Vol. 43(1), January 2014, pp. 82-87 Real Time wave forecasting using artificial neural network with varying input parameter
More informationUse of Neural Networks to Forecast Time Series: River Flow Modeling
Use of Neural Networks to Forecast Time Series: River Flow Modeling RICHARD CHIBANGA 1, JEAN BERLAMONT 2 AND JOOS VANDEWALLE 3 1 Ph.D. student in the Hydraulics Laboratory, Civil Eng. Dept. 2 Prof. and
More informationUncertainty in the SWAT Model Simulations due to Different Spatial Resolution of Gridded Precipitation Data
Uncertainty in the SWAT Model Simulations due to Different Spatial Resolution of Gridded Precipitation Data Vamsi Krishna Vema 1, Jobin Thomas 2, Jayaprathiga Mahalingam 1, P. Athira 4, Cicily Kurian 1,
More informationHydrologic Response of SWAT to Single Site and Multi- Site Daily Rainfall Generation Models
Hydrologic Response of SWAT to Single Site and Multi- Site Daily Rainfall Generation Models 1 Watson, B.M., 2 R. Srikanthan, 1 S. Selvalingam, and 1 M. Ghafouri 1 School of Engineering and Technology,
More informationRAINFALL RUNOFF MODELING USING SUPPORT VECTOR REGRESSION AND ARTIFICIAL NEURAL NETWORKS
CEST2011 Rhodes, Greece Ref no: XXX RAINFALL RUNOFF MODELING USING SUPPORT VECTOR REGRESSION AND ARTIFICIAL NEURAL NETWORKS D. BOTSIS1 1, P. LATINOPOULOS 2 and K. DIAMANTARAS 3 1&2 Department of Civil
More informationEstimation of Reference Evapotranspiration by Artificial Neural Network
Estimation of Reference Evapotranspiration by Artificial Neural Network A. D. Bhagat 1, P. G. Popale 2 PhD Scholar, Department of Irrigation and Drainage Engineering, Dr. ASCAE&T, Mahatma Phule Krishi
More informationfor II. the 728. and
FORECASTING OF EVAPORATION FOR MAKNI RESERVOIRR IN OSMANABAD DISTRICT OF MAHARA SHTRA, INDIA D.T MESHRAM, S.D.. GORANTIWAR, A.D.KULKARNI, P.A.HANGARGEKAR N.R Centre on Pomegranate, Shelgi, Solapur, India.
More informationShort-Term Load Forecasting Using ARIMA Model For Karnataka State Electrical Load
International Journal of Engineering Research and Development e-issn: 2278-67X, p-issn: 2278-8X, www.ijerd.com Volume 13, Issue 7 (July 217), PP.75-79 Short-Term Load Forecasting Using ARIMA Model For
More informationA Feature Based Neural Network Model for Weather Forecasting
World Academy of Science, Engineering and Technology 4 2 A Feature Based Neural Network Model for Weather Forecasting Paras, Sanjay Mathur, Avinash Kumar, and Mahesh Chandra Abstract Weather forecasting
More informationEstimation of extreme flow quantiles and quantile uncertainty for ungauged catchments
Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007. 417 Estimation
More informationTime Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis
American Journal of Environmental Sciences 5 (5): 599-604, 2009 ISSN 1553-345X 2009 Science Publications Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis
More informationStudy of Time Series and Development of System Identification Model for Agarwada Raingauge Station
Study of Time Series and Development of System Identification Model for Agarwada Raingauge Station N.A. Bhatia 1 and T.M.V.Suryanarayana 2 1 Teaching Assistant, 2 Assistant Professor, Water Resources Engineering
More informationHydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT
Hydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT Technical briefs are short summaries of the models used in the project aimed at nontechnical readers. The aim of the PES India
More informationAppendix D. Model Setup, Calibration, and Validation
. Model Setup, Calibration, and Validation Lower Grand River Watershed TMDL January 1 1. Model Selection and Setup The Loading Simulation Program in C++ (LSPC) was selected to address the modeling needs
More informationForecasting of Rain Fall in Mirzapur District, Uttar Pradesh, India Using Feed-Forward Artificial Neural Network
International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 2 Issue 8ǁ August. 2013 ǁ PP.87-93 Forecasting of Rain Fall in Mirzapur District, Uttar Pradesh,
More informationMulti-output ANN Model for Prediction of Seven Meteorological Parameters in a Weather Station
J. Inst. Eng. India Ser. A (October December 2014) 95(4):221 229 DOI 10.1007/s40030-014-0092-9 ORIGINAL CONTRIBUTION Multi-output ANN Model for Prediction of Seven Meteorological Parameters in a Weather
More informationCalibration of a Rainfall Runoff Model to Estimate Monthly Stream Flow in an Ungauged Catchment
Computational Water, Energy, and Environmental Engineering, 2015, 4, 57 66 Published Online October 2015 in SciRes. http://www.scirp.org/journal/cweee http://dx.doi.org/10.4236/cweee.2015.44006 Calibration
More informationWater Availability Analysis: Case Study of Lift Irrigation Scheme
Water Availability Analysis: Case Study of Lift Irrigation Scheme Vidya Purandare 1, Dr.V.H.Bajaj 2 Associate Professor, Faculty of Social Sciences, Water & Land Management Institute, Aurangabad, Maharashtra,
More informationENGINEERING HYDROLOGY
ENGINEERING HYDROLOGY Prof. Rajesh Bhagat Asst. Professor Civil Engineering Department Yeshwantrao Chavan College Of Engineering Nagpur B. E. (Civil Engg.) M. Tech. (Enviro. Engg.) GCOE, Amravati VNIT,
More informationSARIMA-ELM Hybrid Model for Forecasting Tourist in Nepal
Volume-03 Issue-07 July-2018 ISSN: 2455-3085 (Online) www.rrjournals.com [UGC Listed Journal] SARIMA-ELM Hybrid Model for Forecasting Tourist in Nepal *1 Kadek Jemmy Waciko & 2 Ismail B *1 Research Scholar,
More informationData and prognosis for renewable energy
The Hong Kong Polytechnic University Department of Electrical Engineering Project code: FYP_27 Data and prognosis for renewable energy by Choi Man Hin 14072258D Final Report Bachelor of Engineering (Honours)
More informationANN-Based Forecasting of Hydropower Reservoir Inflow
ANN-Based Forecasting of Hydropower Reservoir Inflow Antans Sauhats, Roman Petrichenko, Zane Broka, Karlis Baltputnis, Dmitrijs Sobolevskis Institute of Power Engineering Riga echnical University Riga,
More informationLongshore current velocities prediction: using a neural networks approach
Coastal Processes II 189 Longshore current velocities prediction: using a neural networks approach T. M. Alaboud & M. S. El-Bisy Civil Engineering Dept., College of Engineering and Islamic Architecture,
More informationFrequency Forecasting using Time Series ARIMA model
Frequency Forecasting using Time Series ARIMA model Manish Kumar Tikariha DGM(O) NSPCL Bhilai Abstract In view of stringent regulatory stance and recent tariff guidelines, Deviation Settlement mechanism
More informationPrediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks
Int. J. of Thermal & Environmental Engineering Volume 14, No. 2 (2017) 103-108 Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks M. A. Hamdan a*, E. Abdelhafez b
More informationDeep Learning Architecture for Univariate Time Series Forecasting
CS229,Technical Report, 2014 Deep Learning Architecture for Univariate Time Series Forecasting Dmitry Vengertsev 1 Abstract This paper studies the problem of applying machine learning with deep architecture
More informationMURDOCH RESEARCH REPOSITORY
MURDOCH RESEARCH REPOSITORY http://researchrepository.murdoch.edu.au/86/ Kajornrit, J. (22) Monthly rainfall time series prediction using modular fuzzy inference system with nonlinear optimization techniques.
More informationFLOOD FORECASTING ON THE HUMBER RIVER USING AN ARTIFICIAL NEURAL NETWORK APPROACH
FLOOD FORECASTING ON THE HUMBER RIVER USING AN ARTIFICIAL NEURAL NETWORK APPROACH by Haijie Cai, B. Eng, M. ASc A thesis submitted to the School of Graduate Studies in partial fulfillment of the requirements
More informationNeuroevolution methodologies applied to sediment forecasting
38 Water Quality and Sediment Behaviour of the Future: Predictions for the 21st Century (Proceedings of Symposium HS2005 at IUGG2007, Perugia, July 2007). IAHS Publ. 314, 2007. Neuroevolution methodologies
More informationMULTI MODEL ENSEMBLE FOR ASSESSING THE IMPACT OF CLIMATE CHANGE ON THE HYDROLOGY OF A SOUTH INDIAN RIVER BASIN
MULTI MODEL ENSEMBLE FOR ASSESSING THE IMPACT OF CLIMATE CHANGE ON THE HYDROLOGY OF A SOUTH INDIAN RIVER BASIN P.S. Smitha, B. Narasimhan, K.P. Sudheer Indian Institute of Technology, Madras 2017 International
More informationParticle Swarm Optimization Feedforward Neural Network for Hourly Rainfall-runoff Modeling in Bedup Basin, Malaysia
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol:09 No:10 9 Particle Swarm Optimization Feedforward Neural Network for Hourly Rainfall-runoff Modeling in Bedup Basin, Malaysia
More informationTIME SERIES MODELING OF MONTHLY RAINFALL IN ARID AREAS: CASE STUDY FOR SAUDI ARABIA
American Journal of Environmental Sciences 10 (3): 277-282, 2014 ISSN: 1553-345X 2014 Science Publication doi:10.3844/ajessp.2014.277.282 Published Online 10 (3) 2014 (http://www.thescipub.com/ajes.toc)
More informationPrediction of Seasonal Rainfall Data in India using Fuzzy Stochastic Modelling
Global Journal of Pure and Applied Mathematics. ISSN 0973-1768 Volume 13, Number 9 (2017), pp. 6167-6174 Research India Publications http://www.ripublication.com Prediction of Seasonal Rainfall Data in
More informationDo we need Experts for Time Series Forecasting?
Do we need Experts for Time Series Forecasting? Christiane Lemke and Bogdan Gabrys Bournemouth University - School of Design, Engineering and Computing Poole House, Talbot Campus, Poole, BH12 5BB - United
More informationDevelopment of Stochastic Artificial Neural Networks for Hydrological Prediction
Development of Stochastic Artificial Neural Networks for Hydrological Prediction G. B. Kingston, M. F. Lambert and H. R. Maier Centre for Applied Modelling in Water Engineering, School of Civil and Environmental
More informationShort-term wind forecasting using artificial neural networks (ANNs)
Energy and Sustainability II 197 Short-term wind forecasting using artificial neural networks (ANNs) M. G. De Giorgi, A. Ficarella & M. G. Russo Department of Engineering Innovation, Centro Ricerche Energia
More informationFloodplain modeling. Ovidius University of Constanta (P4) Romania & Technological Educational Institute of Serres, Greece
Floodplain modeling Ovidius University of Constanta (P4) Romania & Technological Educational Institute of Serres, Greece Scientific Staff: Dr Carmen Maftei, Professor, Civil Engineering Dept. Dr Konstantinos
More informationResults of Intensity-Duration- Frequency Analysis for Precipitation and Runoff under Changing Climate
Results of Intensity-Duration- Frequency Analysis for Precipitation and Runoff under Changing Climate Supporting Casco Bay Region Climate Change Adaptation RRAP Eugene Yan, Alissa Jared, Julia Pierce,
More informationApplication Research of ARIMA Model in Rainfall Prediction in Central Henan Province
Application Research of ARIMA Model in Rainfall Prediction in Central Henan Province Lulu Xu 1, Dexian Zhang 1, Xin Zhang 1 1School of Information Science and Engineering, Henan University of Technology,
More information1. Introduction. 2. Artificial Neural Networks and Fuzzy Time Series
382 IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.9, September 2008 A Comparative Study of Neural-Network & Fuzzy Time Series Forecasting Techniques Case Study: Wheat
More informationSeasonality and Rainfall Prediction
Seasonality and Rainfall Prediction Arpita Sharma 1 and Mahua Bose 2 1 Deen Dayal Upadhyay College, Delhi University, New Delhi, India. e-mail: 1 asharma@ddu.du.ac.in; 2 e cithi@yahoo.com Abstract. Time
More informationComparing the Univariate Modeling Techniques, Box-Jenkins and Artificial Neural Network (ANN) for Measuring of Climate Index
Applied Mathematical Sciences, Vol. 8, 2014, no. 32, 1557-1568 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.4150 Comparing the Univariate Modeling Techniques, Box-Jenkins and Artificial
More informationEvaluating the Performance of Artificial Neural Network Model in Downscaling Daily Temperature, Precipitation and Wind Speed Parameters
Int. J. Environ. Res., 8(4):1223-1230, Autumn 2014 ISSN: 1735-6865 Evaluating the Performance of Artificial Neural Network Model in Downscaling Daily Temperature, Precipitation and Wind Speed Parameters
More informationDownscaling Ensemble Weather Predictions for Improved Week-2 Hydrologic Forecasting
1564 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 12 Downscaling Ensemble Weather Predictions for Improved Week-2 Hydrologic Forecasting XIAOLI LIU AND PAULIN COULIBALY Department of Civil
More informationINDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -33 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.
INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY Lecture -33 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. Summary of the previous lecture Regression on Principal components
More informationSOIL MOISTURE MODELING USING ARTIFICIAL NEURAL NETWORKS
Int'l Conf. Artificial Intelligence ICAI'17 241 SOIL MOISTURE MODELING USING ARTIFICIAL NEURAL NETWORKS Dr. Jayachander R. Gangasani Instructor, Department of Computer Science, jay.gangasani@aamu.edu Dr.
More informationElements of Multivariate Time Series Analysis
Gregory C. Reinsel Elements of Multivariate Time Series Analysis Second Edition With 14 Figures Springer Contents Preface to the Second Edition Preface to the First Edition vii ix 1. Vector Time Series
More informationWavelet Neural Networks for Nonlinear Time Series Analysis
Applied Mathematical Sciences, Vol. 4, 2010, no. 50, 2485-2495 Wavelet Neural Networks for Nonlinear Time Series Analysis K. K. Minu, M. C. Lineesh and C. Jessy John Department of Mathematics National
More informationImproved ensemble representation of soil moisture in SWAT for data assimilation applications
Improved ensemble representation of soil moisture in SWAT for data assimilation applications Amol Patil and RAAJ Ramsankaran Hydro-Remote Sensing Applications (H-RSA) Group, Department of Civil Engineering
More informationEFFICIENCY OF THE INTEGRATED RESERVOIR OPERATION FOR FLOOD CONTROL IN THE UPPER TONE RIVER OF JAPAN CONSIDERING SPATIAL DISTRIBUTION OF RAINFALL
EFFICIENCY OF THE INTEGRATED RESERVOIR OPERATION FOR FLOOD CONTROL IN THE UPPER TONE RIVER OF JAPAN CONSIDERING SPATIAL DISTRIBUTION OF RAINFALL Dawen YANG, Eik Chay LOW and Toshio KOIKE Department of
More informationComparison Forecasting with Double Exponential Smoothing and Artificial Neural Network to Predict the Price of Sugar
Comparison Forecasting with Double Exponential Smoothing and Artificial Neural Network to Predict the Price of Sugar Fauziah Nasir Fauziah *, Aris Gunaryati Universitas Nasional Sawo Manila, South Jakarta.
More informationReliability of Daily and Annual Stochastic Rainfall Data Generated from Different Data Lengths and Data Characteristics
Reliability of Daily and Annual Stochastic Rainfall Data Generated from Different Data Lengths and Data Characteristics 1 Chiew, F.H.S., 2 R. Srikanthan, 2 A.J. Frost and 1 E.G.I. Payne 1 Department of
More informationFORECASTING OF INFLATION IN BANGLADESH USING ANN MODEL
FORECASTING OF INFLATION IN BANGLADESH USING ANN MODEL Rumana Hossain Department of Physical Science School of Engineering and Computer Science Independent University, Bangladesh Shaukat Ahmed Department
More informationCOMPARISON OF CLEAR-SKY MODELS FOR EVALUATING SOLAR FORECASTING SKILL
COMPARISON OF CLEAR-SKY MODELS FOR EVALUATING SOLAR FORECASTING SKILL Ricardo Marquez Mechanical Engineering and Applied Mechanics School of Engineering University of California Merced Carlos F. M. Coimbra
More informationA new method for short-term load forecasting based on chaotic time series and neural network
A new method for short-term load forecasting based on chaotic time series and neural network Sajjad Kouhi*, Navid Taghizadegan Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz,
More informationParameter estimation of an ARMA model for river flow forecasting using goal programming
available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/jhydrol Parameter estimation of an ARMA model for river flow forecasting using goal programming Kourosh Mohammadi a, *, H.R.
More informationReal time wave forecasting using neural networks
Real time wave forecasting using neural networks M.C. Deo *, C. Sridhar Naidu Department of Civil Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400 076, India Abstract Forecasting
More informationth Hawaii International Conference on System Sciences
2013 46th Hawaii International Conference on System Sciences Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models Applications at Multiple Geographically
More informationINDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.
INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. Course Contents Introduction to Random Variables (RVs) Probability Distributions
More informationApplication of Levenberg-Marquardt Optimization Algorithm Based Multilayer Neural Networks for Hydrological Time Series Modeling
An International Journal of Optimization and Control: heories & Applications Vol.1, No.1, pp.53-63 (2011) IJOCA ISSN 2146-0957 http://www.ijocta.com Application of Levenberg-Marquardt Optimization Algorithm
More informationA Hybrid ARIMA and Neural Network Model to Forecast Particulate. Matter Concentration in Changsha, China
A Hybrid ARIMA and Neural Network Model to Forecast Particulate Matter Concentration in Changsha, China Guangxing He 1, Qihong Deng 2* 1 School of Energy Science and Engineering, Central South University,
More informationUncertainty assessment for short-term flood forecasts in Central Vietnam
River Basin Management VI 117 Uncertainty assessment for short-term flood forecasts in Central Vietnam D. H. Nam, K. Udo & A. Mano Disaster Control Research Center, Tohoku University, Japan Abstract Accurate
More informationArgument to use both statistical and graphical evaluation techniques in groundwater models assessment
Argument to use both statistical and graphical evaluation techniques in groundwater models assessment Sage Ngoie 1, Jean-Marie Lunda 2, Adalbert Mbuyu 3, Elie Tshinguli 4 1Philosophiae Doctor, IGS, University
More informationCARPE DIEM CENTRE FOR WATER RESOURCES RESEARCH DELIVERABLE 9.5(NUID-CWRR) PRECIPITATION DATA ANALYSIS AND ERRORS DRAFT
CARPE DIEM CENTRE FOR WATER RESOURCES RESEARCH DELIVERABLE 9.5(NUID-CWRR) PRECIPITATION DATA ANALYSIS AND ERRORS DRAFT by Micheal Bruen & Benoit Parmentier December 2004 Department of Civil Engineering
More informationTime Series and Forecasting
Time Series and Forecasting Introduction to Forecasting n What is forecasting? n Primary Function is to Predict the Future using (time series related or other) data we have in hand n Why are we interested?
More informationDevelopment of a Monthly Rainfall Prediction Model Using Arima Techniques in Krishnanagar Sub-Division, Nadia District, West Bengal
Page18 Development of a Monthly Rainfall Prediction Model Using Arima Techniques in Krishnanagar Sub-Division, Nadia District, West Bengal Alivia Chowdhury * and Amit Biswas ** * Assistant Professor, Department
More informationShort Term Solar Radiation Forecast from Meteorological Data using Artificial Neural Network for Yola, Nigeria
American Journal of Engineering Research (AJER) 017 American Journal of Engineering Research (AJER) eiss: 300847 piss : 300936 Volume6, Issue8, pp8389 www.ajer.org Research Paper Open Access Short Term
More informationDirect Method for Training Feed-forward Neural Networks using Batch Extended Kalman Filter for Multi- Step-Ahead Predictions
Direct Method for Training Feed-forward Neural Networks using Batch Extended Kalman Filter for Multi- Step-Ahead Predictions Artem Chernodub, Institute of Mathematical Machines and Systems NASU, Neurotechnologies
More informationResearch Article Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge
Artificial Intelligence Volume, Article ID 68628, pages doi:.//68628 Research Article Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge M. Mohan Raju, R. K. Srivastava,
More informationMODELLING ENERGY DEMAND FORECASTING USING NEURAL NETWORKS WITH UNIVARIATE TIME SERIES
MODELLING ENERGY DEMAND FORECASTING USING NEURAL NETWORKS WITH UNIVARIATE TIME SERIES S. Cankurt 1, M. Yasin 2 1&2 Ishik University Erbil, Iraq 1 s.cankurt@ishik.edu.iq, 2 m.yasin@ishik.edu.iq doi:10.23918/iec2018.26
More informationComparison of three back-propagation training algorithms for two case studies
Indian Journal of Engineering & Materials Sciences Vol. 2, October 2005, pp. 434-442 Comparison of three back-propagation training algorithms for two case studies Özgür Kişi a & Erdal Uncuoğlu b a Hydraulics
More informationForecasting of Nitrogen Content in the Soil by Hybrid Time Series Model
International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 07 (2018) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2018.707.191
More informationInfluence of spatial variation in precipitation on artificial neural network rainfall-runoff model
Hydrology Days 212 Influence of spatial variation in precipitation on artificial neural network rainfall-runoff model André Dozier 1 Department of Civil and Environmental Engineering, Colorado State University
More informationImproved the Forecasting of ANN-ARIMA Model Performance: A Case Study of Water Quality at the Offshore Kuala Terengganu, Terengganu, Malaysia
Improved the Forecasting of ANN-ARIMA Model Performance: A Case Study of Water Quality at the Offshore Kuala Terengganu, Terengganu, Malaysia Muhamad Safiih Lola1 Malaysia- safiihmd@umt.edu.my Mohd Noor
More informationLinear genetic programming for time-series modelling of daily flow rate
Linear genetic programming for time-series modelling of daily flow rate Aytac Guven Civil Engineering Department, Gaziantep University, 27310 Gaziantep, Turkey. e-mail: aguven@gantep.edu.tr In this study
More information