Modelling and short term forecasting of flash floods in an urban environment

Abstract

Rapid urbanization, climate change and extreme rainfall has resulted in a growingnumber of cases of urban flash floods. It is important to predict the occurrenceof flood so that the aftermath of the flood can be minimized. Flood forecasting isa major exercise performed to determine the chances of a flood when suitableconditions are present. Short term forecasting or nowcasting is a dominant techniqueused in urban cities for prediction of the very near future incident up to sixhours. In orthodox methods of flood forecasting, current weather conditions areexamined using conventional methods such as use of radar, satellite imaging andcomplex calculation involving complicated mathematical equations.Recent developments in Information and Communication Technology(ICT) andMachine Learning(ML) has helped us to study this hydrological problem alongwith many real world situation in different perspective. The main aim of thisthesis is to design a theoretical model that accounts parameters causing an urbanflash flood and develop a prediction tool for the forecasting of near future event.To test the soundness of a model, data syntheses is performed and the results areseen using the artificial neural network.