The objectives of this study are to conduct an analysis on rainfall change tendencies, calculate the inundation in the basins of Mun and Chi Rivers in the northeastern region of Thailand, and clarify the flood risk in the long term, taking the spatial characteristics of flooding into consideration. To grasp the rainfall change tendencies, two statistical analyses are conducted using the Mann-Kendall test and the generalized extreme value distribution. The inundation analysis is conducted using the Rainfall-Runoff-Inundation (RRI) model. As a result of the statistical analysis on the rainfall characteristics, it can be observed that the annual rainfall has significant increasing tendencies at the significance level of 5% in a wide area of the upper reaches. In addition, inundation calculation indicates that the maximum inundation depth and inundation area have increased in recent years.
Using deep learning to identify meteorological factors has enabled optimal predictions of Thailand's seasonal precipitation two months in advance. A combination of surface temperature and pressure, specific humidity, and wind speed (zonal and meridional components) was tested.Examining each combination of meteorological factor has created optimal input data for seasonal precipitation forecasts. In addition, the hyperparameters of each model were calculated by Bayesian optimization. Predictive model performance tended to be better when the weight for pressure was higher, while a higher weight for specific humidity reduced predictive performance. Finally, visualization of the positive neuron values in all the coupled layers of the first layer showed that the regions with the highest frequency of occurrence were the El Niño monitoring areas such as the "Indian Ocean Basin Wide" (IOBW) and "NINO WEST".
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