EVALUATING DROUGHT USING MACHINE LEARNING AND HYDROLOGICAL MODEL BY INCORPORATING SATELLITE-BASED PRECIPITATION DATA IN THE UNGAUGED BASIN

Author

Anjan Parajuli, Southern Illinois University CarbondaleFollow

Date of Award

8-1-2024

Degree Name

Master of Science

Department

Civil Engineering

First Advisor

Kalra, Ajay

Abstract

Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), and standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. The precipitation data to calculate the SPI is obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a Hydrological model for calculating discharge and drought in the overall basin and uses Random Forest (RF) and Support Vector Regression (SVR) as a machine learning model for SSI prediction for a time scale of 1- and 3-month period, which is widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the result obtained from the Machine Learning Models. The result from the prediction model, hydrological model, and the station data shows a better correlation. Moreover, the result revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one. The results and discussion in this research will aid planners and decision-makers in managing hydrological drought in basins.