Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective

Abstract

This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine learning (ML) models – artificial neural network (ANN), recurrent neural network (RNN), and adaptive fuzzy neural inference system (ANFIS) – for streamflow prediction under the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways (SSPs), specifically SSP245 and SSP585. Four key performance indicators, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), guide the evaluation. These models employ monthly precipitation, maximum and minimum temperatures as inputs, and discharge as the output, spanning 1985–2014. The ANN model with a 3-10-1 architecture outperforms RNN and ANFIS, displaying lower MSE, RMSE, MAE, and higher R2 values for both training (MSE = 20417, RMSE = 142, MAE = 71, R2 = 0.94) and testing (MSE = 9348, RMSE = 96, MAE = 108, R2 = 0.92) datasets. Subsequently, the superior ANN model predicts streamflow up to 2100 using SSP245 and SSP585 scenarios. These results underscore the potential of ANN models for robust futuristic streamflow estimation, offering valuable insights for water resource management and planning.

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

“…The machine learning models are extensively used for streamflow forecasting due to several reasons namely minimal input data requirement and reduced computation time. These models are widely used in data‐scarce regions (Khan, Khan, Khan, & Khan, 2023; Khan, Khan, Khan, Khan, Haleem, & Khan, 2023; Khan, Khan, Khan, Khan, Khan, & Khan, 2023). Das and Nanduri (2018) combined support vector machine (SVM) and relevance vector machine (RVM) models with the CMIP5 GCMs to project streamflow in India.…”