Prediction of Water Quality Index by Support Vector Machine: a Case Study in the Sefidrud Basin, Northern Iran

Kamyab-Talesh, Forough; Mousavi, Seyed Farhad; Khaledian, Mohammadreza; Yousefi-Falakdehi, Ozra; Norouzi-Masir, Mojtaba

doi:10.1134/s0097807819010056

Cited by 26 publications

(10 citation statements)

References 20 publications

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“…The Support Vector Machine (SVM) model is also believed to be a very powerful machine learning technique for both linear and nonlinear regression problems and has been used in various scientific issues with high prediction accuracy [40]. [45] Studied the optimization of the SVM model to identify the major parameters that significantly affect the WQI and found that Nitrate is the major parameter for WQI prediction. In a study of prediction WQI in constructed wetlands, SVM and two other AI method were used and the results had shown that the SVM result predicted WQI with high accuracy than the two other models [26]- [31], [46]- [54].…”

Section: Support Vector Machines/regressionmentioning

confidence: 99%

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

Aminu¹

2022

Global J. Eng. Technol. Adv.

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The development of computer models for water quality index forecasting has been a leading research topic worldwide which has been considerably recognized over the last two decades; the balance between efficient water quality requires a good water management technique. The balance is said to be achieved through various procedures many of which require the application of computer-aided forecasting tools. In this paper, a decade research review on the water quality index in the field of artificial intelligence was carried out with the aim to present the most viable or most suitable methods and models to be adopted for future researchers in the field of water quality. The review incorporates the developed models such as ANN, ANFIS, SVM, other regression or time-series, and other soft computing models. This research shows that the study focused on a decade review of the methods and models, and also, there is room for long-term forecasts. It also shows that there is no single AI model that outperforms all the remaining AI models but It is necessary to evaluate the strength of each model combination for each region thus to know what type of method or model that works best for the country or region. The use of AI has grown significantly in recent decades however there is enough room for researchers to duel in and improve in the field of water quality index.

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Section: Support Vector Machines/regressionmentioning

confidence: 99%

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

Aminu¹

2022

Global J. Eng. Technol. Adv.

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“…3 below, it can be identified that DO, BOD, pH and NO3 are regarded as the significant input parameters for accurate representation of WQ, further concluded from [151][152][153] that the parameters with the highest significance are dissolved oxygen and ammoniacal nitrogen. Kamyab-Talesh, et al [140] identified NO3 as the most important attribute for WQI with subsequent importance in BOD and TDS. Nonetheless, the study in Bayatzadeh Fard, et al [120] regarded fecal coliform the most significant parameter for water quality classification, as the study has proven that a great error might occur if FC is omitted.…”

Section: B Water Quality Predictionmentioning

confidence: 99%

“…Other than ANN and ANFIS models, Kamyab-Talesh, et al [140] proposed the stability of SVM model that results to 87% of total variability and lower bias. However, the training process of SVM is rather laborious as all classes require optimization.…”

Section: ) Water Quality Modellingmentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

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“…It requires fewer samples and possesses convenient modeling, simple calculations, short learning and training times, and strong versatility; therefore, it can be used to solve the groundwater quality evaluation problem belonging to pattern recognition [21]. In SVM applications, its performance is directly affected by the selection of the model parameters [22]; therefore, the optimal search of the best parameters is particularly important.…”

Section: Introductionmentioning

confidence: 99%

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

Ni¹,

Yao²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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The scientific evaluation of water quality change trends and pollution characteristics is of great significance to improving the current situation of water resources. The particle swarm optimization support vector machine based on principal component analysis (PCA-PSO-SVM) was used to conduct a comprehensive evaluation of groundwater quality in Xinzhou city, and the results were compared with those of a variety of traditional water quality evaluation methods. The evaluation results show that the water quality evaluation model based on PCA-PSO-SVM is more comprehensive and objective. The overall groundwater quality situation in Xinzhou city is generally good. The upper pore water quality is mainly class II, and the lower karst water is mainly class III. The water quality shows significant spatial differences. Compared with the traditional water quality evaluation method, the improved SVM algorithm compensates for the defects of the traditional method. The model structure is stable, and the accuracy and calculation efficiency are high, which makes the method worthy of promotion and application. The research results can provide a scientific basis and reference value for the water quality evaluation of groundwater-related projects.

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Prediction of Water Quality Index by Support Vector Machine: a Case Study in the Sefidrud Basin, Northern Iran

Cited by 26 publications

References 20 publications

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

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

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