Drought Analysis and Forecasting in Odisha using Machine Learning Techniques

Abstract

Drought is a natural phenomenon that damages agricultural land severely. The severity of drought must be reduced to decrease its impact on agricultural productivity. The study of drought was carried out for the state Odisha which experienced drought 8 times during the last 20 years due to failure of monsoon. Analysis for the data was explored by explorative analysis.The drought forecasting was carried out using machine learning techniques like the Auto-regressive model (AR), Long Short-Term Memory (LSTM), and Auto-regressive Integrated Moving Average (ARIMA) using daily rainfall data collected for 28 years (1993-2020). Further using this data each district was categorised into four different categories namely Flood (FL), No Drought (ND), Moderate Drought (MD), and Severe Drought (SD). To classify the districts after forecasting, classification models were used like Support Vector Classifier (SVC) and Naïve Bayes. The results of the forecasting model as well as the classification model were compared. It becomes important to forecast drought for proper planning and management of the water resource system to decrease the damage due to such calamities. This study is valuable for the government, farmers, and other stakeholders to understand the pattern and reason behind the severity of drought to take relevant precautionary measures and improve decisions and facilities to tackle such natural calamities.