GIS - based automated flood forecast modelling application using climatic data in Daduru Oya river basin

Abstract

Flood is the most common and deadliest form of disaster that affects lives and properties all around the world. Predicting natural disasters is very complex due to lack of proper methods and resources in countries like Sri Lanka. But if there is an efficient prediction system it helps to save not only lives but the environment and infrastructure too. Therefore, the aim of this study is to pave the pathway to build an efficient and effective flood prediction system through analysing available flood modelling techniques and their applications to find their strengths and weaknesses. Then the result of the study could be used to put the foundation for the main requirement of building the system to predict natural disasters. To achieve this the GIS technology, Big data analytic and IoT with machine learning techniques, two-dimensional hydrodynamic flood models, statistical models, rainfall-runoff models, Fuzzy-neuro approach and data mining and data analysis applications were analysed by a thorough review of available recent literature. A generic model was developed to take any DEM data and a pour point feature class layer for the specific DEM to generate outputs based on other variable that could input to the model. It gave model calibration capability as well as significant time saving on tasks. Use of special tools like ‘Parse Path’ tool, gave the capability to name outputs easily and quickly. And it also made saving so efficient because it automatically saves all the results to the file path of the DEM. Due to these factors, when it starts raining in upper catchment area, could forecast due inundation area in minutes. Including the GIS technology could improve the data quality and availability while incorporating different data sources for more in-depth analysis could give more accurate predictions. Using GIS based hydrological model, a suitable system to implement in Sri Lanka could be developed.