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By: Sandaru G. M. C. and Illeperuma I. A. K. S.
1Student, Department of Remote Sensing and GIS, Faculty of Geomatics, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka.
2Senior Lecturer, Department of Remote Sensing and GIS, Faculty of Geomatics, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka.
Abstract
Flood is the most common natural disaster of Sri Lanka. Sri Lanka is suffering from extensive flood events more frequently than previously due to climatic changes. Due to rapid urbanization, there are limited land resources to live in Kalutara district which is not prone to flood events. This research aims to model the flood susceptibility in Kalutara district Sri Lanka which is having frequent flood events using Logistic Regression Machine Learning model. Rainfall, Elevation, Slope, Aspect, Stream Power Index (SPI), Topographic Wetness Index (TWI), Distance to Rivers, River Water Level were selected as flood contribution factors. Flood extent was extracted using SAR images captured from Sentinel 1 using Google Earth Engine (GEE). Elevation data was downloaded from ALOS PALSAR dataset. Logistic Regression model fitting was done using python language with some open-source libraries. Fitted Logistic Regression model was able to achieve 0.89 Area Under the Curve (AUC) value in the Receiver Operating Characteristic (ROC) curve. The results show that flood susceptibility is very high around the major water features which are Kalu Ganga and Kuda Ganga. Among the other factors, River Water Level is having the highest contribution to the flood susceptibility in the area. Rainfall does not make much contribution to the flood susceptibility over the area.
Keywords: Flood, flood susceptibility, Google Earth engine, logistic regression, machine learning, SAR, sentinel
Citation:
Refrences:
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