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The severe flooding in Thailand in 2011 was triggered by the tropical storm Nock-ten at end of July along the Mekong and Chao Phraya river basin. There are 4 additional storms that caused medium to heavy rainfall from June to October in the north and north-east of Thailand. Due to limited capacity of the Chao Phraya river and also Pasak river, several overbank flows occurred and also dikes along the river were broken causing excessive flow to many communities beside the river and downstream. The consequence was a total of 815 deaths with 13.6 million people affected and over 20,000 km2 farmland devastated, the inundation remained until mid-January 2012. Total estimated cost of economic lost was about 45.7 billion US$ with respect to manufacturing industry as seven major industrial estate in the northern provinces of Bangkok were submerged 2-3 m during high flood. This caused interruption to supply chain to car parts regionally and world-wide, e.g. electronic components and hard disk drives. Will Bangkok experience more intense rainfall under the changing climate? The Artificial Neural Network (ANN) technique is adopted in this study to shed some lights on this issue. ANN is an established technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data. The present study utilizes ANN to statistically downscale global climate models (GCMs) at some meteorology stations in Bangkok. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from the ERA-Interim reanalysis data, meteorological station data (predictand), present and future GCM data of certain emission scenarios. The findings will certainly be useful to the policy makers in pondering, e.g. whether the current drainage network system is sufficient to meet the changing climate, and a range of flood adaptation and mitigation measures.


Session R66, Hydrology and Climate Change II



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