Multiple Linear Regression and Machine Learning for Predicting the Drinking Water Quality Index in Al-Seine Lake

Review Computer Science, Interdisciplinary Applications

Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications

Dani Irwan et al.

Summary: This review addresses issues related to water quality prediction methods and the application of artificial intelligence models. The study compares 83 research articles and finds that deep learning models outperform traditional machine learning models. Additionally, the potential of generative adversarial networks and attention-based transformer in improving water quality prediction is discussed.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2023)

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Article Environmental Sciences

Prediction of Water Quality Classification of the Kelantan River Basin, Malaysia, Using Machine Learning Techniques

Nur Hanisah Abdul Malek et al.

Summary: Machine Learning (ML) is a widely studied and utilized technology that allows processing of large amounts of data and handling non-linear structures through complex mathematical computations. Traditional ML models, however, face challenges like bias and overfitting, which calls for advanced techniques like bagging and boosting. This study demonstrates the implementation of diverse ML models to predict water quality classification in Kelantan River and finds that the Gradient Boosting model performs the best, achieving high accuracy, sensitivity, and f-measure.

WATER (2022)

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Article Water Resources

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Saber Kouadri et al.

Summary: Groundwater quality appraisal is essential for safe drinking water sources, and the study utilized 8 artificial intelligence algorithms to predict water quality index in Illizi region, southeast Algeria. Key drivers influencing WQI were identified as total dissolved solids (TDS) and total hardness (TH), with different input combinations and analysis strategies impacting time consumption and error rates in WQI computation.

APPLIED WATER SCIENCE (2021)

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Editorial Material Environmental Sciences

What Role Does Hydrological Science Play in the Age of Machine Learning?

Grey S. Nearing et al.

Summary: This paper is derived from a keynote talk given at Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall-runoff simulation show that there is more information in large-scale hydrological data sets than previously thought. The paper calls for the hydrology community to focus on developing a quantitative understanding of the value of hydrological process understanding in a modeling discipline increasingly dominated by machine learning.

WATER RESOURCES RESEARCH (2021)

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Article Engineering, Environmental

Analysis of water quality indices and machine learning techniques for rating water pollution: a case study of Rawal Dam, Pakistan

Mehreen Ahmed et al.

Summary: Water Quality Index (WQI) is an effective technique for assessing water quality, but most indices are not universally applicable due to biases towards specific attributes. The study calculated WQIs for different time periods and used machine learning algorithms to classify water quality, finding that uneven sample distribution led to data imbalance. The Decision Tree algorithm performed best in water quality classification, highlighting the need to address uncertainties and biases in data acquisition and class imbalance.

WATER SUPPLY (2021)

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Article Environmental Sciences

Prediction of water quality parameters using machine learning models: a case study of the Karun River, Iran

Atefeh Nouraki et al.

Summary: Different machine learning models were used to predict TDS, SAR, and TH in the Karun River system, with random forest regression showing the best performance for TDS, support vector regression for SAR, and multiple linear regression for TH. The study proved the effectiveness of the machine learning models in accurately estimating water quality in the river system.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021)

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Article Engineering, Environmental

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Seyed Babak Haji Seyed Asadollah et al.

Summary: The study introduces a new ensemble machine learning model, Extra Tree Regression (ETR), for predicting monthly Water Quality Index (WQI) values at the Lam Tsuen River in Hong Kong. The ETR model generally produces more accurate WQI predictions for both training and testing phases, demonstrating improvement in prediction performance and reduction in the number of input parameters compared to previous approaches.

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING (2021)

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Article Environmental Sciences