RESUMO
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.
Assuntos
Mudança Climática , Redes Neurais de Computação , Aprendizado de Máquina , Rios , Recursos HídricosRESUMO
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.
Assuntos
Solo , Água , Rios , Redes Neurais de Computação , Movimentos da ÁguaRESUMO
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 (Tmax), Minimum Temperature (Tmin), and precipitation for the aforementioned three techniques. The best MME for Tmax, Tmin, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R2 during training (65.25 m3/s, 4256.97 m3/s, 46.793 m3/s and 0.7978) and testing (72.06 m3/s, 5192.95 m3/s, 51.363 m3/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.
Assuntos
Poluentes Atmosféricos/toxicidade , Doenças Cardiovasculares/etiologia , Material Particulado/toxicidade , Doenças Respiratórias/etiologia , Poluentes Atmosféricos/química , Doenças Cardiovasculares/epidemiologia , Exposição Ambiental , Humanos , Tamanho da Partícula , Material Particulado/química , Doenças Respiratórias/epidemiologiaRESUMO
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.
Assuntos
Desenvolvimento Sustentável , Meios de Transporte , China , Desenvolvimento Econômico , Paquistão , PolíticasRESUMO
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.