Groundwater Quality Evaluation Based
on PCA-PSO-SVM Machine Learning
in Xinzhou City, China

Ni, Qihang; Yao, Leihua; Song, Chenguang; Hua, Chengya

doi:10.15244/pjoes/140170

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…PSO is a global optimisation algorithm that can hold discrete and continuous variables [159,160], while SVR is a regression capability. When used together as a hybrid AI system, PSO and SVR can overcome the limitations of each method and improve the accuracy of groundwater quality predictions [161]. The advantages of using POS-SVR in groundwater quantity management include its ability to handle nonlinear relationships and model complex systems and its ability to provide accurate predictions even when the available data is limited or incomplete.…”

Section: Deep Belief Network (Dbn) and Support Vector Regression (Svr)mentioning

confidence: 99%

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: Deep Belief Network (Dbn) and Support Vector Regression (Svr)mentioning

confidence: 99%

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Zaresefat

Derakhshani

2023

Water

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“…The results were related to those of several conventional water quality evaluation techniques. The evaluation's ndings demonstrate how thorough and unbiased the PCA-PSO-SVM-based water quality evaluation model is [31]. A water quality dataset in various locations throughout India was categorized using machine learning techniques like RF, NN, MLR, SVM, and BTM.…”

Section: Introductionmentioning

confidence: 99%

Machine learning aided multiclass classification, regression, and cluster analysis of groundwater quality variables congregated from the YSR district

Mogaraju¹

2023

Preprint

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In this study, machine learning classifiers are integrated with the geostatistical analyses. The data extracted from the surface maps derived from ordinary kriging were passed onto ML algorithms, resulting in prediction accuracies of 95% (Gradient Boosting Classifier) for classification and 91% (Random Forest Regressor) for Regression. Kmeans clustering model provided better results in clustering analysis based on Silhouette, Calinski-Harabasz, and Davies-Bouldin metrics. However, there was certain overfitting in the prediction, probably due to limited data available for analysis. In addition, the interpolation methods might have affected the model performance by producing overfitting and underfitting results. It is to report that the Gradient Boosting classifier in classification mode yielded relatively high prediction accuracies in predicting groundwater quality when three classes were used. The Random Forest Regressor in regression mode returned better results in predicting groundwater quality features when multiple classes were used in this study. This work reports that machine learning algorithms can predict groundwater quality with minimal expense and expertise.

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“…Ni Qihang used the SVM based on principal component analysis to comprehensively evaluate the groundwater quality. Compared with the traditional water quality evaluation method, the improved SVM algorithm made up for the shortcomings of the traditional method, and had good stability, higher accuracy and calculation efficiency [7]. Sakaa Bachir used the sequence minimum optimization SVM and random forest algorithm as the benchmark model for predicting the water quality value of river basins, and found that the random forest generated more accurate water quality index prediction, thus revealing the improvement of the early water quality index prediction tool [8].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Water Pollution Control Engineering Based on Support Vector Machine Model

2020

WPPCP

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With the continuous progress of industrialization, the social economy has shown a rapid growth trend, and the living sewage discharge has also increased, which has led to a decline in the quality of the surface water environment and harmed human health. Water quality has become a major factor restricting the sustainable development of economy and society. In view of the frequent occurrence of water pollution accidents and the deterioration of water environment, people pay more and more attention to the prevention and protection of water resources. However, the traditional manual control method cannot effectively solve the problem of water pollution, so it is necessary to innovate the existing technology. Automatic control technology and information technology can be combined to realize online monitoring of pollutants, so as to achieve real-time monitoring and timely warning. Support Vector Machine (SVM) is an adaptive processing method based on machine learning algorithm. By training the neural network, more accurate results can be obtained. To some extent, it overcomes the shortcomings brought by manual operation and has a broad application prospect in the field of environmental monitoring. This paper combined SVM and artificial neural network to develop a set of water quality automatic monitoring system, and used SVM model to predict sewage concentration, which also combined Back Propagation (BP) network to optimize parameters to improve detection accuracy. Finally, by strengthening the application of technology, a scientific and effective evaluation system was established to achieve the goal of water pollution prevention and control. Compared with traditional prevention and control methods, intelligent water environment treatment based on SVM model could quickly and accurately identify the water quality status, and the work efficiency was also improved by 19.35%. It could quickly and accurately determine whether there were toxic and harmful pollutants in the sewage, thus ensuring the health of the people and reducing the occurrence of environmental pollution events, which provided strong support for environmental protection.

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Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

Cited by 7 publications

References 16 publications

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Machine learning aided multiclass classification, regression, and cluster analysis of groundwater quality variables congregated from the YSR district

Simulation of Water Pollution Control Engineering Based on Support Vector Machine Model

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