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

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.

Intercomparison of SWAT and ANN techniques in simulating streamflows in the Astore Basin of the Upper Indus.

The current research work was carried out to simulate monthly streamflow historical record using Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) at the Astore Basin, Gilgit-Baltistan, Pakistan. The performance of SWAT and ANN models was assessed during calibration (1985-2005) and validation (2006-2010) periods via statistical indicators such as coefficient of determination (R), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and root-mean-square error (RMSE). R, NSE, PBIAS, and RMSE values for SWAT (ANN with Architecture (2,27,1)) models during calibration are 0.