Abstract:
Rainfall forecast is essential for water resources planning and management. Various approaches for forecasting have been developed ; however, the accuracy of the forecast is still not satisfied for real engineering practice. This study aims to investigate the capability of the machine learning approach in the forecasting of monthly rainfall by using Deep Learning Neural Network (DNN). Ping river basin, situated in the northern part of Thailand, was selected as a study area due to its availability of long time series of rainfall data. Six rainfall stations, distributed over the river basin, were selected for analysis using monthly rainfall from 1975 to 2018. Based on previous studies in this area, it has been revealed that 24 Large Atmospheric Variables (LAV), used as predictors in the DNN model, were correlated with monthly rainfall over the Ping river basin. The result of the first simulation using all 24 LAV during the validation period (2009-2018) in predicting monthly rainfall for 6 rainfall stations for one year ahead indicates that DNN is capable of forecasting with an accuracy of the forecast from 58-72%. Further improvement of the forecast was also conducted by the input selection technique resulting in a reduction of input LAV from 24 to 13 LAV. The second simulation of DNN with the input selection technique revealed that DNN provided better accuracy of the forecast for one year ahead with the stochastic efficiency of the forecast from 70-78%.
Thammasat University. Thammasat University Library