{"title":"利用贝叶斯神经网络预测地下水质量指数并确定关键变量","authors":"Saumen Maiti, Surabhi Gupta, Praveen Kumar Gupta","doi":"10.1007/s11270-024-07459-w","DOIUrl":null,"url":null,"abstract":"<p>Prediction of groundwater quality index (GWQI) and quantification of the influence of each key water quality variable is vital for water resource management. This paper shows the potential of the automatic relevance determination-based Bayesian neural network(ARD-BNN) technique for GWQI prediction and compares the predictive performance of ARD-BNN with artificial neural network (ANN), long short-term memory network (LSTM), convolutional neural network (CNN) and hybrid CNN-LSTM. What discriminates the present approach from the previous approach is that GWQI is simulated from the global criteria of WHO and the artificial intelligence (AI) models are trained and cross-validated in a synthetically simulated data set of 1470 examples and applied to predict GWQI using newly collected groundwater samples from an area surrounding Amarpur dolerite dyke, Dhanbad, Jharkhand (INDIA). The present analysis suggests that ARD-BNN is relatively skilful (<i>MSE</i><sub>ARD-BNN</sub> = 0.01) relative to other AI models investigated (<i>MSE</i><sub>ANN</sub> = 0.06;<i>MSE</i><sub>CNN</sub> = 1.60; <i>MSE</i><sub>LSTM</sub> = 3.85;<i>MSE</i><sub>CNN-LSTM</sub> = 8.21). The efficacy of the method and stability of results is also tested in the presence of different levels of correlated noise which suggests that the ARD-BNN model is considerably unwavering for up to 20% correlated noise; however, adding more noise (∼50% or more) degrades the results. Sensitivity analysis via ARD-BNN-based soft-pruning strategy identifies that [NO<sub>3</sub><sup>−</sup>], [SO<sub>4</sub><sup>2−</sup>],[pH],[F<sup>−</sup>]and [EC], are key water quality variables for predicting GWQI in the study area.</p>","PeriodicalId":808,"journal":{"name":"Water, Air, & Soil Pollution","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Groundwater Quality Index and Identification of Key Variables Using Bayesian Neural Network\",\"authors\":\"Saumen Maiti, Surabhi Gupta, Praveen Kumar Gupta\",\"doi\":\"10.1007/s11270-024-07459-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Prediction of groundwater quality index (GWQI) and quantification of the influence of each key water quality variable is vital for water resource management. This paper shows the potential of the automatic relevance determination-based Bayesian neural network(ARD-BNN) technique for GWQI prediction and compares the predictive performance of ARD-BNN with artificial neural network (ANN), long short-term memory network (LSTM), convolutional neural network (CNN) and hybrid CNN-LSTM. What discriminates the present approach from the previous approach is that GWQI is simulated from the global criteria of WHO and the artificial intelligence (AI) models are trained and cross-validated in a synthetically simulated data set of 1470 examples and applied to predict GWQI using newly collected groundwater samples from an area surrounding Amarpur dolerite dyke, Dhanbad, Jharkhand (INDIA). The present analysis suggests that ARD-BNN is relatively skilful (<i>MSE</i><sub>ARD-BNN</sub> = 0.01) relative to other AI models investigated (<i>MSE</i><sub>ANN</sub> = 0.06;<i>MSE</i><sub>CNN</sub> = 1.60; <i>MSE</i><sub>LSTM</sub> = 3.85;<i>MSE</i><sub>CNN-LSTM</sub> = 8.21). The efficacy of the method and stability of results is also tested in the presence of different levels of correlated noise which suggests that the ARD-BNN model is considerably unwavering for up to 20% correlated noise; however, adding more noise (∼50% or more) degrades the results. Sensitivity analysis via ARD-BNN-based soft-pruning strategy identifies that [NO<sub>3</sub><sup>−</sup>], [SO<sub>4</sub><sup>2−</sup>],[pH],[F<sup>−</sup>]and [EC], are key water quality variables for predicting GWQI in the study area.</p>\",\"PeriodicalId\":808,\"journal\":{\"name\":\"Water, Air, & Soil Pollution\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water, Air, & Soil Pollution\",\"FirstCategoryId\":\"6\",\"ListUrlMain\":\"https://doi.org/10.1007/s11270-024-07459-w\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water, Air, & Soil Pollution","FirstCategoryId":"6","ListUrlMain":"https://doi.org/10.1007/s11270-024-07459-w","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Prediction of Groundwater Quality Index and Identification of Key Variables Using Bayesian Neural Network
Prediction of groundwater quality index (GWQI) and quantification of the influence of each key water quality variable is vital for water resource management. This paper shows the potential of the automatic relevance determination-based Bayesian neural network(ARD-BNN) technique for GWQI prediction and compares the predictive performance of ARD-BNN with artificial neural network (ANN), long short-term memory network (LSTM), convolutional neural network (CNN) and hybrid CNN-LSTM. What discriminates the present approach from the previous approach is that GWQI is simulated from the global criteria of WHO and the artificial intelligence (AI) models are trained and cross-validated in a synthetically simulated data set of 1470 examples and applied to predict GWQI using newly collected groundwater samples from an area surrounding Amarpur dolerite dyke, Dhanbad, Jharkhand (INDIA). The present analysis suggests that ARD-BNN is relatively skilful (MSEARD-BNN = 0.01) relative to other AI models investigated (MSEANN = 0.06;MSECNN = 1.60; MSELSTM = 3.85;MSECNN-LSTM = 8.21). The efficacy of the method and stability of results is also tested in the presence of different levels of correlated noise which suggests that the ARD-BNN model is considerably unwavering for up to 20% correlated noise; however, adding more noise (∼50% or more) degrades the results. Sensitivity analysis via ARD-BNN-based soft-pruning strategy identifies that [NO3−], [SO42−],[pH],[F−]and [EC], are key water quality variables for predicting GWQI in the study area.
期刊介绍:
Water, Air, & Soil Pollution is an international, interdisciplinary journal on all aspects of pollution and solutions to pollution in the biosphere. This includes chemical, physical and biological processes affecting flora, fauna, water, air and soil in relation to environmental pollution. Because of its scope, the subject areas are diverse and include all aspects of pollution sources, transport, deposition, accumulation, acid precipitation, atmospheric pollution, metals, aquatic pollution including marine pollution and ground water, waste water, pesticides, soil pollution, sewage, sediment pollution, forestry pollution, effects of pollutants on humans, vegetation, fish, aquatic species, micro-organisms, and animals, environmental and molecular toxicology applied to pollution research, biosensors, global and climate change, ecological implications of pollution and pollution models. Water, Air, & Soil Pollution also publishes manuscripts on novel methods used in the study of environmental pollutants, environmental toxicology, environmental biology, novel environmental engineering related to pollution, biodiversity as influenced by pollution, novel environmental biotechnology as applied to pollution (e.g. bioremediation), environmental modelling and biorestoration of polluted environments.
Articles should not be submitted that are of local interest only and do not advance international knowledge in environmental pollution and solutions to pollution. Articles that simply replicate known knowledge or techniques while researching a local pollution problem will normally be rejected without review. Submitted articles must have up-to-date references, employ the correct experimental replication and statistical analysis, where needed and contain a significant contribution to new knowledge. The publishing and editorial team sincerely appreciate your cooperation.
Water, Air, & Soil Pollution publishes research papers; review articles; mini-reviews; and book reviews.