Application of machine learning in groundwater quality modeling - A comprehensive review

Haggerty, Ryan; Sun, Jiayuan; Yu, Hui; Li, Yusong

doi:10.1016/j.watres.2023.119745

Cited by 109 publications

(26 citation statements)

References 193 publications

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“…Clustering: Clustering groups similar data points [103]. Clustering can identify areas with similar water quality characteristics or group water samples based on their chemical compositions in quality groundwater management [105]. 4.…”

Section: Basic Tasks and Groundwatermentioning

confidence: 99%

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Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Zaresefat

Derakhshani

2023

Water

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Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge.

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Section: Basic Tasks and Groundwatermentioning

confidence: 99%

“…Anomaly detection: This task involves identifying data points that deviate significantly from the norm. In quality groundwater management, anomaly detection can identify areas with unusual water quality characteristics or detect changes in water quality over time [105]. 5.…”

Section: Basic Tasks and Groundwatermentioning

confidence: 99%

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Zaresefat

Derakhshani

2023

Water

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“…Machine learning models have many advantages over statistical models in making accurate predictions, but unlike statistical models, few studies have addressed the issues of modeling left-censored variables with machine learning models. There is a clear need for this since machine learning models are becoming increasingly popular in predicting pollutants because of their advantages over traditional statistical models, such as their ability to simulate the complex nonlinear response between drivers and response − and often more accurate prediction performance. , Moreover, some studies applied machine learning models to simulate trace pollutants without accounting for the issues with left-censoring. In those studies, data preprocessing by substitution or discretization is required, − which can introduce bias and other limitations as discussed in statistical models.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Wu,

Liang,

Qiu

2023

ACS EST Water

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Trace pollutants are widely observed in aquatic ecosystems and can significantly impact human health and the environment. Accurate prediction of trace pollutants and understanding their response to environmental drivers are key to environmental management, yet these tasks remain challenging. An important reason for this challenge is that monitoring data for trace pollutants are often left-censored, leading to biased estimation and inaccurate response-driver relationships. Here we propose a novel two-stage interpretable machine learning framework applicable to left-censored trace pollutant data. The two stages include (1) a classifier to predict the presence of the pollutant and (2) a regressor to predict the pollutant concentration if present. The two stages were followed by a model interpretation to understand the contribution of drivers to the presence and concentration of the pollutant accordingly. We take the prediction of microcystin (MICX) in lakes across the United States as a case study. Applying this framework to MICX consistently improved prediction accuracy, including prediction of its occurrence and concentration regardless of the algorithms and performance metrics used. The best-performing algorithm using the two-stage framework, compared with the baseline model, improves classification performance by 48% to 290% and the regression performance by 11% to 33%, depending on the metric used to evaluate the performance. The interpretable machine learning model also successfully revealed the impacts of the most important drivers on the presence of MICX and its concentration. Our results showed the advantages of this framework, including its interpretability to understand the driver-response relationship, ability to handle nonlinearity, better prediction performance, differentiation between the underlying processes, and potential to be generalized to other pollutants. As such, we anticipate that the framework we propose will be a starting point for using state-of-the-art interpretable machine learning models for predicting trace pollutants.

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“…Groundwater, as an essential water resource, plays a crucial role in ensuring the sustainable circulation of the water system [1] . With the development of the economy, groundwater resources have already been subjected to varying degrees of anthropogenic contamination.…”

Section: Introductionmentioning

confidence: 99%

Water quality evaluation of water sources based on artificial neural network

You,

Zhao,

Wang

2023

International Conference on Internet of Things and Machine Learning (IoTML 2023)

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Application of machine learning in groundwater quality modeling - A comprehensive review

Cited by 109 publications

References 193 publications

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Water quality evaluation of water sources based on artificial neural network

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