3 Assistant Professor, Sri Venkateswara College of Engineering and Technology, Chittoor, AP, India,

Transcription

1 ISSN Vol.04,Issue.06, June-2016, Pages: Weather Forecasting By Weighted Bayesian Approach AUDIREDDY GAYATHRI 1, J. VELMURUGAN 2, M. REVATHI 3 1 PG Scholar, Sri Venkateswara College of Engineering and Technology, Chittoor, AP, India, gayathriaudireddys@gmail.com. 2 Associate Professor, Sri Venkateswara College of Engineering and Technology, Chittoor, AP, India, velmurugan85@gmail.com. 3 Assistant Professor, Sri Venkateswara College of Engineering and Technology, Chittoor, AP, India, mrevathi30@gmail.com. Abstract: Weather forecasting is one of the application of science and technology, predicts the state of atmosphere for a given location. Weather warnings are important for forecasting because they are used to protect life and property. Forecasting based on climate variables such as temperature, precipitation and other variables. This can play a vital role in agriculture and other areas. Here we use classification and prediction methods where it initially focuses on getting the data from different methodological sources where thereby further be given to the systems for preprocessing. This can handle inconsistent and noisy data to be processes. Few of these classification techniques such as Bayesian classifier and Decision Tree can be used in obtaining the predicted data. Also the recorded data i.e., present data can be used as an input to the classification and prediction tools. By considering these methodologies appropriate results will be obtained in forecasting the weather. Keywords: Weather forecasting, Prediction, Bayesian Classifier, Decision Tree. I. INTRODUCTION The Datamining in the field of Climatology includes many of important key terms which are bold to detect this field. This also specifies the nature of the weather, its usage and importance and in depicting the future happenings[13 ] in before through the use of voluminous number of techniques. These techniques can be enriched by few terms in the integrated development environment as NetBeans as discussed below. The behavior of a global weather phenomenon is often described by different techniques[4] and methodologies. However, now days we can also using or considering different sensors to collect weather information and Radars, as well as we may also using satellites to understand the nature of weather. The climate scientists [5] and geologists may use different mathematical and statistical tools or methods to predict the future weather changes by considering present and past data set, to which that data can be applied to statistical methods. In the process of forecasting weather there is consideration of previous and present data of particular area to take appropriate decision, and this data can be simply known as dataset or weather data. Forecasting[3] can in simple be defined as the process of collection of data on state of atmosphere for the given location according to time, and which records the humidity, rainfall, temperature, dew point, wind etc. forecasting also refers as prediction. Forecasting [15] may be done for the days or on weeks based on the need of user. This mainly forecasts the future weather events by applying data mining techniques [12] for the present datasets of considered area. To forecast the future weather, firstly classifies the data and then prediction will be done. We can obtain the previous recorded datasets from the metrological data centers and from the online weather portals of our country or may others. The major objectives of this project encompasses related to the applying of the new and better approaches compared to the other kind of algorithms applied. To forecast the future using one of the best datamining approaches as Weighted Bayesian Approach. This acquires the effective[7] and efficient results as here the attributed are assigned with some weights and any one among is attribute with key prominence is assigned with the highest weight among all the attributes. The paper is structured by sections. The Section 1 includes basic introduction regarding the paper. The section 2 describes regarding the literature survey. The section 3 includes the dataset collection details for implementation. The section 4 describes the related work carried out. And the section 5 includes the procedure regarding the proposed system. The section 6 describes the results obtained. The conclusion and the future work is defined in section 7. II. LITERATURE SURVEY There have been done many studies regarding this clustering, detection of dipoles, tele connections, forecasting and so on. Among those a paper by Ashwini Mandale portrays the approach of classification rules [16] including various methods as decision tree, CART algorithms defining their functionalities. This also represents the resultant data obtained finally in the form of rules. The paper proposed by 2016 IJIT. All rights reserved.

2 Sharma of Weather forecasting using soft computing includes a new approach through image generation obtaining the [19] resultant adverse conditions through fuzzy logics. The paper given by Jyothsna Devi, ANN Approach for weather prediction presents the approach related to as the back propagation [20] used for calculating the large amount of data or series of attributes resulting in the feed forward networks generated along with the various parameters. These are few of related works done on this weather forecasting by the use of different approaches. III. DATASET COLLECTION The dataset from the heterogeneous data resources as weather ground portal for the location of India country. These dataset consist of six five parameters as temperature, dew point, humidity, sea level pressure and wind and one dependent parameter as event presented in the following table. Here, the dependent attribute is the class label of particular row or tuple. Past data was taken for days, weeks and months because the weather changes rapidly within a short period of time. These datasets [17] can be considered from either the portals depicting the country datasets or by approaching with any of the meteorological centers. IV. RELATED WORK In the related work of the existing system the back propagation [1] approach is used forming the different layers as multi layer feed forward networks [6]. In this approach different parameters are considered as in the input layers resulting in the final output layer as variations of different climatic parameters based on the respective parameters considered [8]. V. PROPOSED SYSTEM The Proposed System includes the weather forecasting by the use of weighted Bayesian approach[9]. This approach includes some series of steps as summarized below, The initial step of this approach implementation includes the extraction or collection of dataset from the meteorological database or from the historical database. Dataset of weather include the parameters like temperature, humidity, dew point and etc. This data is considered from the initial stages by changing the raw data into useful data. This useful data obtained is used for the further process of algorithm for the future weather forecasting according to the user needs finally. This data will be store in database for descritization purpose and also to apply algorithms. Descritization helps to convert the numerical values into categorical values as low, medium and high. Decision tree induction [2] used to select best attribute to classify the data for this it uses the information gain as its attribute selection measure. Here, which attribute have the highest information gain that was chosen as splitting attribute. Decision tree induction also helps to minimize the information needed to classify the given tuple. There are several decision tree induction algorithms for attribute selection as Id3, C4.5 and CART. These algorithms reduce the amount of AUDIREDDY GAYATHRI, J. VELMURUGAN, M. REVATHI information needed analysis the past data trends to find out the future happenings. For all these considered attributes as temperature, dew point, humidity, sea level pressure any one of these attribute have the highest importance based on the measurement of highest information gain. Splitting attribute consider the only important data to classify the given data. This algorithm works for the numerical attributes. Next step is to apply the Weighted Bayesian Classification [10] for the considered past data. Weights are allocated for the attributes by using above mentioned decision tree induction methods. Weights can also be given as random numbers because we don t have knowledge to give importance for only one attribute. It will help us to give weights to attributes or parameters. In our proposed system if the temperature variable has highest information gain it may be considered as important variable and to give more weight to this attribute. (1) Bayesian classification [11] results that probability of a given tuple belongs to a particular class. Bayesian classification applied for large datasets and it also give accurate results when compare to other algorithms. This algorithm consider the independence assumption on attributes, it means that effect of one attribute is independent of other attributes in the dataset. Bayesian algorithm [14] works for both the categorical values and the numerical values. This calculates the probability of considered data instance belonging to the particular class specified. (3) Here if the above condition gets satisfied then it states that x belongs to class i (C i ). Finally, this algorithm obtains the probability values for each data instance in the dataset to generate the label respectively. Label represent Future event of particular data instance may be the sunny day, rainy day and normal day. Some of the numerical values not found in the dataset then that values are replaced by using Laplacian correction. Laplacian correction avoids the problem of getting probability values of zero. Algorithm: Input: D: a dataset of training data P: Probability values of occurrences of class labels with respect attributes W: weights of the attributes Output: Classify the given tuple and forecast future event. (2)

3 Begin data preprocessing Fill and discritize the training sample data and Testing samples; construction of classifier Analyze the data set and find out a probability values on the conditional attributes and decision attributes in the data set and generate probability table. Generate the Bayesian mode for classification, and learn its parameter; Compute each prior probability P(X/C i ), it is, the probability of each given conditional attribute X under decision attribute it prediction attribute Y; Compute the weight values, examine the training set, Calculate the information need for the classification of given tuple in our data set D is Info (D) = - ) Through this we find out the information gain for each attribute in the dataset is Weather Forecasting By Weighted Bayesian Approach VI. EXPERIMENTAL RESULTS The results our approach provides the probability values for given days belongs to normal days is as given in below table. TABLE I: Probability Values For Normal Event Gain (C) = Info (D) Info c (D) attribute weight for each attribute{ compute P(X/C i ) = P(X 1 /C i ) W 1 P(X 2 /C i ) W 2. P (X n /C i ) W n } if P (C i / X) > P (C j / X) satisfied Fig.1. Graph depicting probability of occurrence of Normal Event. In the above Fig.1 depicted the X-axis co-ordinates refer to the different tuples as defined in the above table, whereas the y-axis represents the respective probability values. TABLE II: Probability Values for Rain Event {//check for class membership probabilities Tuple belongs to class C i Else { Tuple belongs to class C j} end for} classification estimation Obtain the important attribute through giving different weights to each attribute. End Thus the above algorithm describes the complete implementation procedure from the calculation of probabilities of tuple to its respective events forecasting. Fig.2. Graph depicting probability of occurrence of Rain Event.

4 AUDIREDDY GAYATHRI, J. VELMURUGAN, M. REVATHI TABLE III: Probability Values for of Fog Event VII. CONCLUSION Weather forecasting is very challenging problem in now a day s because of very dynamic nature of weather. Weather forecasting plays a vital role in agriculture and other areas. Weighted Bayesian Classification approach is easy to understand and easy to implement classification technique. By considering these methodologies appropriate results will be obtained in forecasting the weather. In this project consider the weights for attributes to forecast future based on important variables. This approach effectively has done the classification and prediction of future by the training and analysis of historical data. In future it can be extended to forecasting of the occurrence of extreme events using the various better approaches and also applied for prediction of large areas at a time. Classification techniques will be considered as an alternative to the traditional metrological approaches and for the real-time applications. Fig.3. Graph depicting probability of occurrence of Fog Event. TABLE IV: Probability Values for Different Occurrence of Events The above table and Figs.2 to 4 depicts the values for particular tuples and their respective probability values provided by the Weighed Bayesian Approach. The tuples L, H, M and P represents the temperature, humidity, dew point and other variable values. The numerical values of that variable discretized as low (L), medium (M) and high (H) and etc. ultimately our system forecast the day belong to rainy day, normal day and others. Fig.4.Probability of different events by weighted Bayesian approach. VIII. ACKNOWLEDGMENT I whole-heartedly express my gratitude and esteemed regards to Mr. J. Velmurugan, Ms. M. Revathi, my parents, friends and well-wishers who helped me in carrying out this work successfully. IX. REFERENCES [1]Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach by Sanjay D. Sawaitul, Prof. K. P. Wagh, Dr.P. N.Chatur Government College of Engineering, Amravati, Maharashtra, India. [2]Decision Tree for the Weather Forecasting by Rajesh Kumar, Ph.D Asst. Prof., Dept. of ECS Dronacharya College of Engineering, Gurgaon, India. [3]Rainfall Prediction using data mining by Sangari.R.S, Dr.M.Balamurugan School of Computer Science and Engineering, Bharathidasan University, Trichy, India. [4]Data Mining Techniques for Weather Prediction by Divya Chauhan, Jawahar Thakur Department of Computer Science,Himachal Pradesh University Shimla, India. [5]Towards a Self-Configurable Weather Research and Forecasting System by Khalid Saleem, S. Masoud Sadjadi, Shu-Ching Chen,School of Computing and Information Sciences, Florida International University, Miami FL. [6]An Efficient Weather Forecasting System using Artificial Neural Network by Dr. S. Santhosh Baboo and I.Kadar Shereef. [7]Convective weather forecast accuracy analysis at center and sector levels by yao wang and banavar sridhar, nasa ames research center, moffett field, California. [8]Artificial Neural Networks Application in Weather Forecasting Using RapidMiner by A Geetha, G M Nasira,Mother Teresa Women s University,Kodaikanal. [9]Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting by Nayyar A. Zaidi, Jesus Cerquides, Mark J. Carman Geoffrey I. Webb, Monash University VIC 3800, Australia. [10]Locally Weighted Naive Bayes by Eibe Frank, Mark Hall, and Bernhard P fahringer Department of Computer Science, University of Waikato Hamilton, New Zealand.

5 Weather Forecasting By Weighted Bayesian Approach [11]A Tutorial on Naive Bayes Classification by Choochart Haruechaiyasak. [12]Prediction of rainfall using Data mining technique over Assam by Pinky saikia dutta, hitesh tahbilder, Guwahati University,Gauhati, Assam, India. [13]Prediction of Severe Thunderstorms applying Neural Network using RSRW Data by Himadri Chakrabarty, Sonia Bhattacharya Panihati Mahavidyalaya Barasat State University Kolkata, India. [14]Heart Disease Prediction System using Naive Bayes by Dhanashree S. Medhekar, Mayur P. Bote, Shruti D. Deshmukh. [15]Air Temperature Forecasting using Radial Basis Functional Artificial Neural Networks I.El-feghi, Zakaria Suliman zubi, A. Abozgaya, University of Tripoli,Tripoli- Libya. [16]Efficient Mining of Intertransaction Association Rules, Anthony K.H. Tung, Member, IEEE, Hongjun Lu, Member, IEEE, Jiawei Han, Member, IEEE, and Ling Feng, Member, IEEE. [17]Accuweather.com, [18] [19]Neural Networks and Back Propagation Algorithm Mirza Cilimkovic,Institute of Technology Blanchardstown Blanchardstown Road North Dublin 15 Ireland. [20]Temperature and Humidity Data Analysis for Future Value Prediction using Clustering Technique: An Approach by Badhiye S. S., Dr. Chatur P. N. Wakode B. V. Government College of Engineering,Amravati, Maharashtra, India. Author s Profile: Mr. J. Velmurugan working as an Associate Professor, Department of Computer Science and Engineering in Sri Venkateswara College of Engineering and Technology, Chittoor. His Area of interest is Data Mining. He is pursuing his Ph.D in VIT University, Vellore and had his M.Tech in Dr. M.G.R. University, Chennai. He obtained Professional membership in ISTE Life Member and has teaching work experience of 8 years. Ms. M.Revathi working as an Assistant professor, Department of Computer Science and Engineering in Sri Venkateswara College of Engineering and Technology, Chittoor. She had her M.Tech in MITS, Madanapalli. Her area of interests are Web Technologies and Data Mining. She has teaching work experience of 4 years. Ms. Audireddy Gayathri received her B.Tech in 2012 in Information Technology, affiliated with JNTUA, Anantapuramu. She is now pursuing her M.Tech in Department of Computer Science and Engineering, Sri Venkateswara College of Engineering and Technology, Chittoor. Her area of interest is Data Mining.