Year: 2024
Type: article
Abstract: Droughts significantly impact water resources and agriculture, leading to economic losses and potential human fatalities. This study aims to predict droughts by analysing changes in the Standardised Precipitation Evapotranspiration Index (SPEI) for the Kabul River basin using data from 1981 to 2022. The research is divided into three phases: calculating SPEI, splitting the dataset into training (80%) and testing (20%) subsets, and evaluating model performance. Various machine learning algorithms, including XGBoost, Decision tree, AdaBoost, and KNN, were employed alongside different climatic variables. The models were assessed using statistical metrics such as R², RMSE, MAE, MSE for regression, and confusion matrix, accuracy, precision, recall, F1 score, ROC AUC, and Log loss for classification. Results showed strong performance, with R² values of 0.97, 0.86, 0.92, and 0.96 for XGBoost, KNN, Decision tree, and AdaBoost, respectively. SPEI demonstrated significant potential for drought forecasting, and spatial distribution mapping revealed persistent moderate drought occurrences.
Source: Journal of Agrometeorology
Authors: UZAIR KHAN, ALAMGIR KHALIL, SHABIR JAN
Institution: University of Engineering and Technology Peshawar
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402106824