A Neural Network Model for Surface Air Temperature Estimation over the Eastern Part of Thailand in 2004

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1 A Neural Networ Model for Surface Air Temperature Estimation over the Eastern Part of Thailand in 2004 Wattana Kanbua 1*, Montri Inthachot 2 1 Marine Meteorological Center, Thai Meteorological Department, Bango 10260, Thailand 2 Faculty of Information Technology, Rangsit University, Pathumthani 12000, Thailand watt_an@hotmail.com * ABSTRACT This paper investigates the use of an Artificial Neural Networ (ANN) to estimate one year surface air temperature over the eastern part of Thailand in ANN's are becoming increasingly prominent in many areas of weather forecasting due to their potential to capture the complex relationships between the many factors that contribute to certain weather conditions. The estimations produced by the ANN's were compared to meteorological observation data by the Thai Meteorological Department. The results confirm that ANN's have the potential for successful application to the problem of surface air temperature estimation. The purpose of this study is studying influence of climate change which effected with air temperature in eastern part of Thailand. We have used soft computing approach such as ANN and the result of experimental is very well. We can use ANN technique to predict air temperature for 1 year ahead. Keywords Artificial Neural Networ; surface air temperature; weather forecasting; Thai Meteorological Department 1. INTRODUCTION Thailand's climate is tropical, high both in temperature and humidity, and dominated by monsoons. April and May are the hottest months of the year, when even the locals are moved to complain about the heat. June sees the beginning of the South West Monsoon, and brings with it the rainy season, which continues intermittently until the end of October. From November to the end of February the climate is much less trying with a cooling North East breeze and a reduction in the humidity level. This is also the main tourist season, and the best time to visit Thailand. The north and north-east are generally cooler than Bango in winter and hotter in summer. The air temperature has noted the threshold value (in tenths of degree Celsius) that defines the above, below and near normal categories. Each category, the forecast value is above normal as the temperature is forecast to be warmer (greater) than the climatological value plus the threshold value, below normal as the temperature is forecast to be colder (less) than the climatological value minus the threshold value and near normal as the temperature is forecast to be near the climatological value plus or minus the threshold value. It has to be noted that the surface air temperature forecast is a prediction of the anomaly of the mean daily temperature at 2 metres (i.e. at standard observation Stevenson screen height). It is not a forecast of the maximum nor of the minimum daily temperature. The normal of air temperature is degree Celsius in Thailand. 2. THE EVENT In 2004, there was drought in eastern part of Thailand. Dry conditions occurred in this area and there was no more amount of rainfall in rainy season in 2004 also. This research presents the idea of data analysis and data application about fundamental of temperature forecast in overview of eastern part of Thailand. Normally, drought of Thailand usually occurs in summer season every year. We are studying caused of drought and lac of water for using in consuming and industrial, especially in eastern part of Thailand in There was extremely lac of water budget in many reservoirs and some where has the water lower than normal level meanwhile there was amount of rainfall in rainy season, lower than average amount of rainfall in normal which effect with extremely drought in The eastern part region is eastern sea broad which is important by having tourism places, fishery source, agriculture area and expanding of industrial settlement. The air temperature has relationship with effect on drought becomes important. However, because the response of drought often lags the anomalies of temperature and precipitation by a few months and this lag relationship is not well understood, the questions of how temperature and precipitation affect drought variation and how we may interpret drought in terms of precipitation and temperature anomalies remain. The increasing usage of drought, particularly in climate research and monitoring, demands answers to the above questions and invites further understanding of drought. In this study, we separate, using a theoretical analysis, temperature effects on drought. We use observational and experimental analyses to illustrate temperature effects on drought. We will develop an analytic relationship between drought and temperature. We will show ANN to predict anomalies of yearly temperature.

2 3 ARTIFICIAL NEURAL NETWORK Multi-Layer Perceptron Artificial Neural Networs have become widespread in recent years and the researchers often claim that they provide a useful tool for the predictions of river flow. Three layer networs with sufficient number of hidden nodes are usually applied due to the continuity of the relevant function. Every networ contains an appropriate number of input and output nodes which is equal to the number of input and output variables, and the assumed number of hidden nodes. There is no effective rule for the estimate of the number of hidden nodes. In this study it usually turns out to be close to the number of input nodes, but in each case it is experimentally verified. Figure 1. Multi-Layer Perceptron Artificial Neural Networ scheme Multi-Layer Forward Neural Networ can explain complex data and more multi-layer, when the problem has more complex. However test-train process is to combination of weight of lins. One of methods is popular, is Bacpropagation. Figure 2. Bacpropagation Artificial Neural Networ scheme 3.1 The Bacpropagation Algorithm The bacpropagation algorithm wors in much the same way as the name suggests: After propagating an input through the networ, the error is calculated and the error is propagated bac through the networ while the weights are adusted in order to mae the error smaller. When I explain this algorithm, I will only explain it for fully connected ANNs, but the theory is the same for sparse connected ANNs. Although we want to minimize the mean square error for all the training data, the most efficient way of doing this with the bacpropagation algorithm, is to train on data sequentially one input at a time, instead of training on the combined data. However, this

3 means that the order the data is given in is of importance, but it also provides a very efficient way of avoiding getting stuc in a local minima. I will now explain the bacpropagation algorithm, in sufficient details to allow an implementation from this explanation: First the input is propagated through the ANN to the output. After this the error e on a single output neuron can be calculated as: e = d y (1) Where y is the calculated output and which is again used for adusting the weights. The d is the desired output of neuron. This error value is used to calculate a value is calculated by: value, = e g y ) (2) ( Where g is the derived activation function. The need for calculating the derived activation function was why I expressed the need for a differentiable activation function. When the from the value is calculated, we can calculate the values of this layer. By the following equation: values for preceding layers. The values of the previous layer is calculated K = ηg ( y ) w (3) = 0 Where K is the number of neurons in this layer and η is the learning rate parameter, which determines how much the weight should be adusted. The more advanced gradient descent algorithms does not use a learning rate, but a set of more advanced parameters that maes a more qualified guess to how much the weight should be adusted. Using these values, the Δ w values that the weights should be adusted by, can be calculated by: Δ w = y (4) The Δ w value is used to adust the weight w, by w = w + Δw and the bacpropagation algorithm moves on to the next input and adusts the weights according to the output. This process goes on until a certain stop criteria is reached. The stop criteria is typically determined by measuring the mean square error of the training data while training with the data, when this mean square error reaches a certain limit, the training is stopped. More advanced stopping criteria involving both training and testing data are also used. In this section I have briefly discussed the mathematics of the bacpropagation algorithm, but since this report is mainly concerned with the implementation of ANN algorithms, I have left out details unnecessary for implementing the algorithm. I will refer to [Hassoun, 1995] and [Hertz et al., 1991] for more detailed explanation of the theory behind and the mathematics of this algorithm. 3.2 Running Time of Bacpropagation If the ANN is fully connected, the running time of algorithms on the ANN is dominated by the operations executed for each connection The bacpropagation is dominated by the calculation of the and the adustment of w, since these are the only calculations that are executed for each connection. The calculations executed for each connection when calculating is one multiplication and one addition. When adusting w it is also one multiplication and one addition. This means that the total running time is dominated by two multiplications and two additions (three if you also count the addition and multiplication used in the forward propagation) per connection. This is only a small amount of wor for each connection, which gives a clue to how important it is, for the data needed in these operations to be easily accessible.

4. STUDY AREA We have studied in the eastern region which consists of Chachoengsao, Chanthaburi, Chonburi, Nahon Nayo, Rayong, Prachinburi, Sa Kaeo, Trat provinces during 2004.

1 Analysis and meteorological parameters prediction by using WFNN program The creating of model is to predict such as air temperature and precipitation for 1 year ahead.

4 4. STUDY AREA We have studied in the eastern region which consists of Chachoengsao, Chanthaburi, Chonburi, Nahon Nayo, Rayong, Prachinburi, Sa Kaeo, Trat provinces during In this year there was lac of fresh water because of no more rainfall all this year event rainy season. Figure 3. Thailand Map. Figure 4. Eastern Part of Thailand Map 5. THE RESULTS AND DISCUSSION 5.1 Analysis and meteorological parameters prediction by using WFNN program The creating of model is to predict such as air temperature and precipitation for 1 year ahead. First of all we have to collect and prepare meteorological data, in order to train/test prediction in the model such as air pressure reduced to mean sea level, air temperature dry bulb, wet bulb temperature, dew point temperature, total cloud amount, wind direction, wind speed and rainfall which there are 12 weather stations in eastern part such as Klongyai weather station in Trat province, Chantaburi weather station, Rayong weather station, Laemchabang weather station, Sattahip weather station, Pattaya weather station, Sichang weather station, Chonburi weather station, Sraeaw Weather station, Aranyaprathet weather station, Krabinburi weather station and Prachinburi weather station. In this research shows only air temperature at Rayong province. Train and test of ANN model used to analysis for predict air temperature one year ahead which meteorological data have difference interval data. Therefore it is essential to transform data to become normal, it must modify to be order interval form before it become input data by using Min-Max Normalization method below as: v min A v = (new _ max A new _ min A ) + max A min A new _ min A where v : After transforming value v : Before transforming value max A : maximum value before transforming min A : minimum value before transforming new : maximum value needs to be changed _ max A new _ min A : minimum value needs to be changed 5.2 Weather Forecasting Using Neural Networ (WFNN) In Testing of prediction air temperature is used by ANN model which there is complicated and difficult by using hand. So that we have already developed ANN program, so called Weather Forecasting Using Neural Networ (WFNN) by using JAVA language which it is

able to run on several operating system which have Java Virtual Machine. The program can read raw data in normal form and compute follow as ANN method.

Input nodes contain month, date, time, Pressure mean sea level, air temperature, wet bulb temperature, dew point temperature, total cloud amount, wind direction, wind speed, and Rainfall.

The amount of time that use in learning is 500 epochs. The value of learning rate and momentum are 0.9 and 0.1 respectively. Figure 5. Artificial Neural Networ scheme for air temperatue Figure 6.

5 able to run on several operating system which have Java Virtual Machine. The program can read raw data in normal form and compute follow as ANN method. It can display train-test experiments in graphs form also. 5.3 Parameter The ANN used was a three layer (11-3-1: 11 input nodes, 3 hidden nodes and 1 output node). Input nodes contain month, date, time, Pressure mean sea level, air temperature, wet bulb temperature, dew point temperature, total cloud amount, wind direction, wind speed, and Rainfall. The learning process uses bacpropogation method by use error value has that to turn bac to adust weights and uses sigmoid function as activating function. The amount of time that use in learning is 500 epochs. The value of learning rate and momentum are 0.9 and 0.1 respectively. Figure 5. Artificial Neural Networ scheme for air temperatue Figure 6. GUI of WFNN program. 5.4 Train-test experiments for Air temperature one year ahead This research shows only Rayong province. By showing how ANN learn meteorological data in 2003, in order to predict air temperature in 2004 which is prediction for one year ahead by showing graph of train against with test data. Figure 7. Testing of prediction of training data set during (JAN - APR) Figure 8. Testing of prediction of testing data set during (JAN - APR)

Figure 9. Testing of prediction of air temperature in 2005 (JAN - APR) Figure 10. Testing of prediction of training data set during 2003 2004 (MAY - AUG) Figure 11.

6 Figure 9. Testing of prediction of air temperature in 2005 (JAN - APR) Figure 10. Testing of prediction of training data set during (MAY - AUG) Figure 11. Testing of prediction of testing data set during (MAY - AUG) Figure 12. Testing of prediction in 2005 (JAN - APR) Figure 13. Testing of prediction of training data set during (SEP - DEC) Figure 14. Testing of prediction of testing data set during (SEP - DEC) Figure 15. Testing of prediction air temperature in 2005 (SEP - DEC)

7 The prediction of air temperature for one year ahead can explain past data very well and prediction results about air temperature is impressive. It has still error because long range prediction is not easy to do that, but we have to find out any method to go on. 6. CONCLUSIONS The research presented in this paper considered the effects of changes to the ANN models used to predict air temperature over eastern part of Thailand, which we used Rayong province as representative of eastern region, including larger training set sizes, seasonal input terms, increased lag lengths, and varying the size of the networ. Increasing the size of the training set did not reduce forecast errors. Similar improvements resulted from extending the duration of historical data in the input vector from 6 hours to one year. The introduction of seasonal terms may provide a means of implementing an accurate year-round forecast model. Future wor may compare the accuracy of such models to season specific models such as those created in this research. The decreases in model performance associated with lag lengths greater than 24 hours. Feature extraction methods may be able to reduce the size of the input vector, reducing networ degrees of freedom and improving performance. The results confirm that ANN's have the potential for successful application to the problem of surface air temperature estimation. The purpose of this study is studying influence of climate change which effected with air temperature in eastern part of Thailand. We have used soft computing approach such as ANN and the result of experimental is very well. We can use ANN technique to predict air temperature for 1 year ahead. 7. ACKNOWLEDGEMENTS I would lie to express my sincere gratitude and deep appreciation to Mrs. Prasertsu Chamornmarn for his guidance, invaluable advice, supervision and encouragement throughout this research which enabled me to complete this research successfully. He was never lacing in indness and support. I am particularly indebted to Office of Natural Resources and Environmental Policy and Planning for the financial support which has enabled me to undertae my research. 8. REFERENCES [1] Bryson, A. E., & Ho, Y. C. ( 1969). Applied optimal control. Blaisdel. [2] M. Bell, Dr. A. Giannini, E. Grover-Kopec, Dr. B. Lyon, C. Ropelewsi, Dr. A. Seth, IRI Climate Digest January 2005, Climate Impacts December, Contributions to this page were made by IRI researchers. [3] Derome J., G. Brunet, A. Plante, N. Gagnon, G. J. Boer, F. W. Zwiers, S. J. Lambert, J. Sheng, et H. Ritchie, 2001: Seasonal Predictions Based on Two Dynamical Models.Atmos. Ocean., 39, [4] Hassoun, M. H. (1995), Fundamentals of Artificial Neural Networs. The MIT Press. [5] Hertz, J., Krogh, A., and Palmer, R. G. (1991), Introduction to The Theory of Neural Computing. Addison-Wesley Publishing Company.

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017)

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017) 1. Review of Regional Weather Conditions for November 2017 1.1 In November 2017, Southeast Asia experienced inter-monsoon conditions in the first