Continuous groundwater quality monitoring poses significant challenges affecting the environment and public health. Groundwater in Abidjan, specifically from the Continental Terminal (CT), is the primary supply source...Continuous groundwater quality monitoring poses significant challenges affecting the environment and public health. Groundwater in Abidjan, specifically from the Continental Terminal (CT), is the primary supply source. Therefore, ensuring safe drinking water and environmental protection requires a thorough evaluation and surveillance of this resource. Our present research evaluates the quality of the CT groundwater in Abidjan using the water quality index (WQI) based on the analytical hierarchy process (AHP). This study also explores the application of machine learning predictions as a time-efficient and cost-effective approach for groundwater resource management. Therefore, three Machine Learning regression algorithms (Ridge, Lasso, and Gradient Boosting (GB)) were executed and compared. The AHP-based WQI results classified 98.98% of samples as “good” water quality, while 0.68% and 0.34% of samples were respectively categorized as “excellent” and “poor” water. Afterward, the prediction performance evaluation highlighted that the GB outperformed the other models with the highest accuracy and consistency (MSE = 0.097, RMSE = 0.300, r = 0.766, rs = 0.757, and τ = 0.804). In contrast, the Lasso model recorded the lowest prediction accuracy, with an MSE of 148.921, an RMSE of 6.828, and consistency parameters of r = 0.397, rs = 0.079, and τ = 0.082. Gradient Boosting regression effectively learns nonlinear events and interactions by iteratively fitting new models to errors of previous models, enabling a more realistic groundwater quality prediction. This study provides a novel perspective for improving groundwater quality management in Abidjan, promoting real-time tracking and risk mitigations.展开更多
Starting in the 1960s,a great deal of effort and capital was invested in an endeavor to significantlyimprove the quality of surface water around the United States,particularly in metropolises like New YorkCity,New Yor...Starting in the 1960s,a great deal of effort and capital was invested in an endeavor to significantlyimprove the quality of surface water around the United States,particularly in metropolises like New YorkCity,New York.The Hudson River has been at the forefront of these efforts and has benefited immenselyfrom this campaign.In parts of the developing world,such as China,similar efforts are beginning to gatherthe same type of momentum.Within the past decade,the Huangpu River,which flows through Shanghaiinto the Yangtze Estuary of China,has been a target for ecological restoration and management.It is possibleto draw parallels between the cleanup efforts involved in improving the environmental conditions of theHuangpu and Hudson Rivers.Using the methodologies of series comparison(SCM),water quality index(WQI)modeling,consideration of ecological engineering,and policy strategy design,we comparativelystudied the topography,functions,environmental conditions,ecological engineering countermeasures,andmanagement policies of the two rivers to assist future forecasting of ecological restoration efforts in China.展开更多
文摘Continuous groundwater quality monitoring poses significant challenges affecting the environment and public health. Groundwater in Abidjan, specifically from the Continental Terminal (CT), is the primary supply source. Therefore, ensuring safe drinking water and environmental protection requires a thorough evaluation and surveillance of this resource. Our present research evaluates the quality of the CT groundwater in Abidjan using the water quality index (WQI) based on the analytical hierarchy process (AHP). This study also explores the application of machine learning predictions as a time-efficient and cost-effective approach for groundwater resource management. Therefore, three Machine Learning regression algorithms (Ridge, Lasso, and Gradient Boosting (GB)) were executed and compared. The AHP-based WQI results classified 98.98% of samples as “good” water quality, while 0.68% and 0.34% of samples were respectively categorized as “excellent” and “poor” water. Afterward, the prediction performance evaluation highlighted that the GB outperformed the other models with the highest accuracy and consistency (MSE = 0.097, RMSE = 0.300, r = 0.766, rs = 0.757, and τ = 0.804). In contrast, the Lasso model recorded the lowest prediction accuracy, with an MSE of 148.921, an RMSE of 6.828, and consistency parameters of r = 0.397, rs = 0.079, and τ = 0.082. Gradient Boosting regression effectively learns nonlinear events and interactions by iteratively fitting new models to errors of previous models, enabling a more realistic groundwater quality prediction. This study provides a novel perspective for improving groundwater quality management in Abidjan, promoting real-time tracking and risk mitigations.
基金supported by the Major Project Foundation of China National Social Science(14ZDB140)Fudan-UK Tyndall Foundat ion(FTC98503B03a,Urban Growth i1)+2 种基金the PhD Station Foundation Project of China National Education Minis-try(No.20060246024)the Scientific Innovation Project of Shanghai Municipal Education Commission(No.08ZZ03)the Youth Leadership Program(LINCS)of the State Department,USA,the Foundation of Foho Development Zone,Wujiang,Jiangshu,China,and the Think Tank Program of Fudan University(2015).
文摘Starting in the 1960s,a great deal of effort and capital was invested in an endeavor to significantlyimprove the quality of surface water around the United States,particularly in metropolises like New YorkCity,New York.The Hudson River has been at the forefront of these efforts and has benefited immenselyfrom this campaign.In parts of the developing world,such as China,similar efforts are beginning to gatherthe same type of momentum.Within the past decade,the Huangpu River,which flows through Shanghaiinto the Yangtze Estuary of China,has been a target for ecological restoration and management.It is possibleto draw parallels between the cleanup efforts involved in improving the environmental conditions of theHuangpu and Hudson Rivers.Using the methodologies of series comparison(SCM),water quality index(WQI)modeling,consideration of ecological engineering,and policy strategy design,we comparativelystudied the topography,functions,environmental conditions,ecological engineering countermeasures,andmanagement policies of the two rivers to assist future forecasting of ecological restoration efforts in China.
