Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques

摘要

Natural hazards such as tropical cyclones is a topic of wider interest and operational forecast of their landfall, maximum sustained winds, storm surge height and associated extent of inland inundation is a challenging topic having wider socio-economic implication. Recent advancements in computational power and development of sophisticated models have resulted in better understanding the dynamics of atmosphere and ocean. Forecast quality have improved to a great extent however there are still constraints in computation time and cost for real-time operations. Soft computing techniques in the broad domain of computational intelligence are widely recognized today having diverse practical applications and beneficial value across multiple disciplines. The present study is an effort on application of soft computing techniques such as Artificial Neural Networks, Genetic Algorithm, and Genetic Programming to predict storm surge and inundation characteristics resulting from tropical cyclones. The coast of Odisha adjoining the Bay of Bengal is considered for this case study. Historically, the Odisha coast is known to be highly vulnerable to strike from maximum number of high intense tropical cyclones that form over Bay of Bengal region. Recently in a separate study the authors have developed a comprehensive pre-computed dataset on storm surges and inundation scenarios from historical cyclones that made landfall over coastal Odisha State. Present study is an effort to effectively utilize the pre-computed dataset for real-time operation using soft computing techniques and that performs rapid computation to aid emergency preparedness and planning operations. Study performed several numerical experiments using various soft computing techniques and best possible configuration for real-time operation is developed. An inter-comparison exercise was also carried out to skill assess the performance of various soft computing techniques. The authors believe that this study has immense po