Monitoring water quality is essential for protecting human health and the environment and controlling water quality. Artificial Intelligence (AI) offers significant opportunities to help improve the classification and prediction of water quality (WQ). In this study, various AI algorithms are assessed to handle WQ data collected over an extended period and develop a dependable approach for forecasting water quality as accurately as possible. Specifically, various machine learning classifiers and their stacking ensemble models were used to classify the WQ data via the Water Quality Index (WQI). The studied classifiers included Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), CATBoost, XGBoost, and Multilayer Perceptron (MLP). The dataset used in the study included 1679 samples and their meta-data collected over nine years. In addition, precision-recall curves and Receiver Operating Characteristic curves (ROC) were used to assess the performance of the various classifiers. The findings revealed that the CATBoost model offered the most accurate classifier with a percentage of 94.51. Moreover, after applying stacking ensemble models with all classifiers, accuracy reached 100% in various Meta-classifiers. Furthermore, the CATBoost achieved the highest accuracy as a primary gradient boosting algorithm and a meta classifier. Therefore, the boosting algorithm is proposed as a reliable approach for the WQ classification. The analysis presented in this article presents a framework that can support the efforts of researchers working toward water quality improvement using artificial intelligence. 

Water quality classification using machine learning algorithms

Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: A case study of drinking water quality prediction

Ensuring sufficient free residual chlorine (FRC) up to the time and place water is consumed in refugee settlements is essential for preventing the spread of waterborne illnesses. Water system operators need accurate forecasts of FRC during the household storage period. However, factors that drive FRC decay after water leaves the piped distribution system vary substantially, introducing significant uncertainty when modelling point-of-consumption FRC. Artificial neural network (ANN) ensemble forecasting systems (EFS) can account for this uncertainty by generating probabilistic forecasts of point-of-consumption FRC. ANNs are typically trained using symmetrical error metrics like mean squared error (MSE), but this leads to forecast underdispersion forecasts (the spread of the forecast is smaller than the spread of the observations). This study proposes to solve forecast underdispersion by training an ANN-EFS using cost functions that combine alternative metrics (Nash-Sutcliffe efficiency, Kling Gupta Efficiency, Index of Agreement) with cost-sensitive learning (inverse FRC weighting, class-based FRC weighting, inverse frequency weighting). The ANN-EFS trained with each cost function was evaluated using water quality data from refugee settlements in Bangladesh and Tanzania by comparing the percent capture, confidence interval reliability diagrams, rank histograms, and the continuous ranked probability. Training the ANN-EFS using the cost functions developed in this study produced up to a 70% improvement in forecast reliability and dispersion compared to the baseline cost function (MSE), with the best performance typically obtained by training the model using Kling-Gupta Efficiency and inverse frequency weighting. Our findings demonstrate that training the ANN-EFS using alternative metrics and cost-sensitive learning can improve the quality of forecasts of point-of-consumption FRC and better account for uncertainty in post-distribution chlorine decay. These techniques can enable humanitarian responders to ensure sufficient FRC more reliably at the point-of-consumption, thereby preventing the spread of waterborne illnesses.

https://journals.plos.org/water/article/file?id=10.1371/journal.pwat.0000040&type=printable

Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAI and Big Data in Water EnvironmentsHuichun ZhangHuichun ZhangMore by Huichun ZhangView Biographyhttps://orcid.org/0000-0002-5683-5117Cite this: ACS EST Water 2022, 2, 6, 904–906Publication Date (Web):May 25, 2022Publication History Received10 May 2022Published online25 May 2022Published inissue 10 June 2022https://doi.org/10.1021/acsestwater.2c00203Copyright © 2022 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views370Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertsSUBJECTS:Algorithms,Drinking water,Manufacturing,Nanoparticles,Water treatment Get e-Alerts 

AI and Big Data in Water Environments

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine learning models to predict water quality status in Hong Kong. First, spatial clusters with relatively homogeneous water quality were adaptively detected using dynamically constrained agglomerative clustering and partitioning. Then, the optimal prediction models were constructed for each cluster by locally evaluating the prediction performance of six standalone machine learning models, including multi-layer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), extremely randomized tree (ET), eXtreme gradient boosting (XGBoost) and categorical gradient boosting (CatBoost), as well as four novel hybrid models (MLPNN-SVM, ET-CatBoost, MLPNN-CatBoost and XGBoost-CatBoost). Finally, a sensitivity analysis was conducted to explore the minimum sets of indicative WQPs to achieve more cost-efficient water quality prediction based on locally optimal prediction models. The results revealed that the water quality in the study area was spatially heterogeneous and four spatially contiguous clusters were identified. MLPNN-SVM, ET-CatBoost, MLPNN-CatBoost and CaBboost performed best in Cluster 1 to Cluster 4, with R2 values of 0.917, 0.906, 0.901 and 0.937 and RMSE values of 1.978, 0.843, 2.020 and 1.572, respectively. The results of the sensitivity analysis indicated that acceptable local prediction results can be obtained using fewer WQPs. It is conducive to issuing timely water quality warnings and striving for more time for water pollution remediation. 

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

Abstract:With the development of modern teaching technology, intelligent writing correction platform has emerged and become an effective way to supplement traditional feedback from teachers under the new situation. The main theoretical basis of this study is dynamic evaluation theory. The experimental group and the controlled group  were assigned three writing tasks. The statistical analysis software SPSS was used to analyze the data results. In the study, literature review, experiment, questionnaire and interview methods were used. The literature summarizes the development status of teacher feedback and intelligent platform correction at home and abroad. The experimental method is to compare the similarities and differences between teacher feedback and intelligent platform correction in specific experiments. Through questionnaires and interviews, we can understand the attitudes of teachers and students towards the intelligent correction platform. 
  
The research results show that the two feedback methods are consistent and have obvious differences. Teacher feedback has more significant advantages in the two dimensions of text structure and content, while the intelligent writing correction platform is too general and procedural in the feedback of these two dimensions. In comparison, the feedback of the intelligent writing correction platform on vocabulary and grammar is more detailed, giving students detailed information. The combination of the two ways of feedback and the application of them to the teaching of English writing in senior high schools will have a more ideal effect, which will have a positive effect on the improvement of senior high school students' English writing level. 
Keywords:  Dynamic evaluation theory; Teacher feedback; Feedback from AI writing platform; contrastive study

Houtao Liu

Papers

Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: A case study of drinking water quality prediction

Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

The Study of Feedback in Writing from College English Teachers and Artificial Intelligence Platform Based on Mixed Method Teaching.