Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6.

This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (T), Minimum Temperature (T), and precipitation for the aforementioned three techniques.

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.

Road and Transportation Lead to Better Health and Sustainable Destination Development in Host Community: A Case of China Pakistan Economic Corridor (CPEC).

Road and transportation plays a vital role in the sustainable development and prosperity of the area. This study investigates the impact of road and transportation on the health of the host community and its sustainable destination development. Data were collected from the host community and were analyzed through factor analysis and structure equation modeling to evaluate the in-hand data of the structural relationship.

Engineering risk assessment for emergency disposal projects of sudden water pollution incidents.

Without an engineering risk assessment for emergency disposal in response to sudden water pollution incidents, responders are prone to be challenged during emergency decision making. To address this gap, the concept and framework of emergency disposal engineering risks are reported in this paper. The proposed risk index system covers three stages consistent with the progress of an emergency disposal project.