Data driven-based machine learning modelling and empirical correlations for predicting snow-covered area in the Swat Region, Pakistan

In recent decades, global and regional climate change has emerged as a significant challenge with potential catastrophic consequences, including hurricanes, floods, sea level rise, and temperature shifts. Snow-covered area (SCA) serves as a crucial climatic parameter reflecting climate changes, yet accurately determining SCA proves to be a challenging and time-consuming task. This study aims to develop robust prediction models for SCA by employing three machine learning (ML) approaches using readily available climatic data from Swat, Pakistan, spanning two decades. The climate data encompass precipitation, daily maximum/minimum temperatures, and SCA measurements. Three ML methods—artificial neural networks (ANN), functional networks (FN), and adaptive neuro-fuzzy inference systems (ANFIS)—were employed to model SCA. Accuracy measures, including coefficient of determination (R 2 ), average absolute percentage error (AAPE), and root mean squared error (RMSE) were utilized to evaluate model performance. All three ML models exhibited superior performance, with high R 2 values and low errors. Accuracy indicators of the ANN model are better than FN and ANFIS models, yielding the highest R 2 (0.956) and minimum RMSE and AAPE values (0.61 and 0.91). ANFIS demonstrated slightly better performance than FN, with RMSE, AAPE, and R 2 values of 0.65, 1.1, and 0.950, respectively. FN yielded RMSE, AAPE, and R values of 1.14, 1.72, and 0.85, respectively. Additionally, two empirical correlations were derived from the optimized FN and ANN models for SCA prediction using the same input variables. This study underscores the efficacy of ML techniques in accurately and consistently predicting SCA parameters, offering valuable insights into climate change and its consequences. • The study proposed robust machine learning models for predicting snow-covered areas based on crucial climate parameters. • Sensitivity analysis of the ML models revealed generalization capabilities of proposed the models. • Prediction performance of machine learning models resulting in broader applicability for changing climate. • Mathematical correlations can provide reliable information of the snow-covered area without executing the ML algorithms. • The ML algorithms offer valuable insights into climate change and its consequences in the similar areas in the world.