Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis

Ighalo, Joshua O.; Adeniyi, Adewale George; Marques, Gonçalo

doi:10.1007/s40808-020-01041-z

Cited by 87 publications

(48 citation statements)

References 65 publications

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“…This popularity agrees with the observation of other reviews on the application of ML for WQA [88,106,107]. Apart from their more accessible calibration, robustness, and capability to process nonlinear and complex datasets [106,107], this trend can be attributed to the ANN technique requiring a fairly small amount of data to produce satisfactory prediction results [108]. Even though random forest (RF) and multiple linear regression (MLR) were both predicted eight times, the high number of RF applications can be attributed to the fact that RF is classified as follows [85,109,110]:…”

Section: Commonly Used Modeling Approachessupporting

confidence: 91%

“…Therefore, as seen in Figure 4, it is not surprising that artificial neural network (ANN) related algorithms are the most applied ML techniques. This popularity agrees with the observation of other reviews on the application of ML for WQA [88,106,107]. Apart from their more accessible calibration, robustness, and capability to process nonlinear and complex datasets [106,107], this trend can be attributed to the ANN technique requiring a fairly small amount of data to produce satisfactory prediction results [108].…”

Section: Commonly Used Modeling Approachessupporting

confidence: 85%

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From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Paepae

Bokoro

Kyamakya

2021

Sensors

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Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.

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Section: Commonly Used Modeling Approachessupporting

confidence: 91%

Section: Commonly Used Modeling Approachessupporting

confidence: 85%

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Paepae

Bokoro

Kyamakya

2021

Sensors

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“…The bore wells from which samples were gathered are widely utilized for drinking purposes. Water quality variables, such as pH, TDS, EC, Cl, SO 4 , nitrate, carbonate, bicarbonate, metal ions, and trace elements, have been predicted (Ighalo et al, 2021). They emphasized predicting water quality by using the ML classifier algorithm C5.0, naïve Bayes, and RF as leaners for water quality prediction with high precision and effectiveness.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Soft Computing in Predicting Groundwater Quality Parameters

Hanoon

Ammar

Ahmed

et al. 2022

Front. Environ. Sci.

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Evaluating the quality of groundwater in a specific aquifer could be a costly and time-consuming procedure. An attempt was made in this research to predict various parameters of water quality called Fe, Cl, SO4, pH and total hardness (as CaCO3) by measuring properties of total dissolved solids (TDSs) and electrical conductivity (EC). This was reached by establishing relations between groundwater quality parameters, TDS and EC, using various machine learning (ML) models, such as linear regression (LR), tree regression (TR), Gaussian process regression (GPR), support vector machine (SVM), and ensembles of regression trees (ER). Data for these variables were gathered from five unrelated groundwater quality studies. The findings showed that the TR, GPR, and ER models have satisfactory performance compared to that of LR and SVM with respect to different assessment criteria. The ER model attained higher accuracy in terms of R2 in TDS 0.92, Fe 0.89, Cl 0.86, CaCO3 0.87, SO4 0.87, and pH 0.86, while the GPR model attained an EC 0.98 compared to all developed models. Moreover, comparisons among the different developed models were performed using accuracy improvement (AI), improvement in RMSE (PRMSE), and improvement in PMAE to determine a higher accuracy model for predicting target properties. Generally, the comparison of several data-driven regression methods indicated that the boosted ensemble of the regression tree model offered better accuracy in predicting water quality parameters. Sensitivity analysis of each parameter illustrates that CaCO3 is most influential in determining TDS and EC. These results could have a significant impact on the future of groundwater quality assessments.

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“…According to Ighalo et al (2021), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are the most utilized AI models for water quality monitoring and assessment systems, which include the usage of solar energy, in the last decade. The advantages of using AI methods in various monitoring and assessment application areas are that they are faster and more efficient than linear mathematical or statistical methods, low-cost, can be used for real-time monitoring (Yetilmezsoy et al, 2011;Ighalo et al, 2021;Demetillo et al, 2019), and ensure lower error values in prediction (Karaboga and Kaya, 2019).…”

Section: Introductionmentioning

confidence: 99%

Development of a Fuzzy Inference Based Solar Energy Controller for Smart Marine Water Monitoring

Kalibatiene¹,

Miliauskaitė²,

Dzemydienė³

et al. 2021

Informatica

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Nowadays, there is a lack of smart marine monitoring systems, which have possibilities to integrate multi-dimensional components for monitoring and predicting marine water quality and making decisions for their optimal operations with minimal human intervention. This research aims to extend the smart coastal marine monitoring by proposing a solar energy planning and control component. The proposed approach involves the adaptive neuro-fuzzy inference system (ANFIS) for the wireless buoys, working online during the whole year in the Baltic Sea near the Lithuanian coast. The usage of our proposed fuzzy solar energy planning and control components allows us to prolong the lifespan of batteries in buoys, so it has a positive impact on sustainable development. The novelty and advantage of the proposed approach lie in establishing the ANFIS-based model to predict and control solar energy in a buoy for different lighting and temperature conditions depending on the four year seasons and to make a decision to transfer the collected data. The energy planning and consumption system for the wireless sensor network of buoys is carefully evaluated, and its prototype is developed. The proposed approach can be practically used for environmental monitoring, providing stakeholders with relevant and timely information for sound decision-making about hydro-meteorological situations in coastal marine water.

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Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis

Cited by 87 publications

References 65 publications

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Application of Soft Computing in Predicting Groundwater Quality Parameters

Development of a Fuzzy Inference Based Solar Energy Controller for Smart Marine Water Monitoring

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