Machine learning based marine water quality prediction for coastal hydro-environment management

Deng, Tianan; Chau, Kwok Wing; Duan, Huan‐Feng

doi:10.1016/j.jenvman.2021.112051

Cited by 147 publications

(42 citation statements)

References 64 publications

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“…Under these conditions, k-fold cross validation is considered a good option [48]. 10-fold cross validation is widely used in ANN water quality modelling studies (e.g., [39,49]), while 5-fold cross validation (e.g., [50]) is also used. In some cases, researchers are using less commonly used k values.…”

Section: Discussionmentioning

confidence: 99%

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Hadjisolomou

Stefanidis

Herodotou

et al. 2021

Water

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Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control.

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Section: Discussionmentioning

confidence: 99%

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Hadjisolomou

Stefanidis

Herodotou

et al. 2021

Water

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“…Furthermore, for instance, the DO parameter, the analysis can be adopted in-situ instruments; however, BOD is recorded for at least five days. Accurate prediction of WQ parameters in a study area can save cost, energy, and time; this is why much effort is given to the modeling approaches when predicting these valuable parameters [24]. The modeling approaches are more important in developing countries where the budget for environmental quality assessment and monitoring is low compared to the developed countries.…”

Section: The Significant Of the Selected Case Studymentioning

confidence: 99%

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

et al. 2021

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An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R 2 , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

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“…These three conditions are directly related to the simulation accuracy of models. Secondly, the processes of model construction and calibration are time-consuming and complicated owing to the vast calculations required [19,51]. For the large cross-basin water transfer project like SNWTP, it would cost excessive time, labor and money to set up sampling points along the way to investigate water quality and analyze hydrodynamics and water quality variations, requiring the researchers to make a trade-off between model accuracy and research economy and efficiency.…”

Section: Performance Comparisons Of Machine Learning Models and Other Modelsmentioning

confidence: 99%

Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

Liao

Zang

Wang

et al. 2021

Water

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Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world’s largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir’s water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects.

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Machine learning based marine water quality prediction for coastal hydro-environment management

Cited by 147 publications

References 64 publications

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

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