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. 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.

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 (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.

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. It is revealed that road and transportation has a significant role in the improvement of health. Moreover, income mediates the effects of accessibility and employment on health. This study will help the authorities and policy maker to formulate policy regarding road and transportation that will improve health of the host community and its sustainable development. The study is limited to the seven districts of Hazara division and explores the societal aspect of CPEC on the host community, future researcher may investigate other regions and may select some other variables such as effect on GDP, per capita income, etc.

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. Fuzzy fault tree analysis (FFTA), a logical and diagrammatic method, was developed to evaluate the potential failure during the process of emergency disposal. The probability of basic events and their combination, which caused the failure of an emergency disposal project, were calculated based on the case of an emergency disposal project of an aniline pollution incident in the Zhuozhang River, Changzhi, China, in 2014. The critical events that can cause the occurrence of a top event (TE) were identified according to their contribution. Finally, advices on how to take measures using limited resources to prevent the failure of a TE are given according to the quantified results of risk magnitude. The proposed approach could be a potential useful safeguard for the implementation of an emergency disposal project during the process of emergency response.