Investigating the effects of climate and land-use changes on surface runoff is critical for water resources management. The majority of studies focused on projected climate change effects on surface runoff, while neglecting future land-use change. Therefore, the main aim of this article is to discriminate the impacts of projected climate and land-use changes on surface runoff using the Soil and Water Assessment Tool (SWAT) through the lens of the Upper Indus Basin, Pakistan. Future scenarios of the land-use and climate changes are predicted using cellular automata artificial neural network and four bias-corrected general circulation models, respectively. The historical record (2000–2013) was divided into the calibration period (2000–2008) and the validation period (2009–2013). The simulated results demonstrated that the SWAT model performed well. The results obtained from 2000 to 2013 show that climate change (61.61%) has a higher influence on river runoff than land-use change (38.39%). Both climate and land-use changes are predicted to increase future runoff depth in this basin. The influence of climate change (12.76–25.92%) is greater than land-use change (0.37–1.1%). Global weather data has good applicability for simulating hydrological responses in the region where conventional gauges are unavailable. The study discusses that both climate and land-use changes impact runoff depth and concludes with some suggestions for water resources managers to bring water environment sustainability.

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 (R2), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and root-mean-square error (RMSE). R2, NSE, PBIAS, and RMSE values for SWAT (ANN with Architecture (2,27,1)) models during calibration are 0.80 (0.88), 0.73 (0.82), 15.7 (0.008), and 79.81 (70.34), respectively, while during validation, the corresponding values are 0.71 (0.86), 0.66 (0.95), 17.3 (0.10), and 106.26 (75.92). The results implied that the ANN model is superior to the SWAT model based on the statistical performance indicators. The SWAT results demonstrated an underestimation of the high flow and overestimation of the low flow. Comparatively, the ANN model performed very well in estimating the general and extreme flow conditions. The findings of this research highlighted its potential as a valuable tool for accurate streamflow forecasting and decision-making. The current study recommends that additional machine learning models may be compared with the SWAT model output to improve monthly streamflow predictions in the Astore Basin.

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.

Efficient and safe transport by air, rail, road and sea plays a major role in countries’ economic and societal self-development. Transport’s role is pivotal in entire regions’ collegial prosperity and socio-economic capabilities. This case study investigates the perceived influence of the China-Pakistan Economic Corridor (CPEC), on the Pakistani educational sector. Data were collected from 505 questionnaire respondents, residing in the Hazara Division, of the Khyber-Pakhtunkhwa Province, in Northern Pakistan. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) are used to analyze the empirical data. The study’s results show that CPEC’s perceived accessibility and employment opportunities might have significantly positive effects on educating the host communities. Besides, people’s increased remuneration can undoubtedly mediate the effect of accessibility and employment on their education. Those collegially responsible for CPEC can benefit from these results, particularly in their formulating educational development plans and coping with unforeseen issues. The article also provides useful guidelines for CPEC practitioners and researchers.