A review of the application of machine learning in water quality evaluation

Eco-Environment & Health, Journal Year: 2022, Volume and Issue: 1(2), P. 107 - 116

Published: June 1, 2022

With the rapid increase in volume of data on aquatic environment, machine learning has become an important tool for analysis, classification, and prediction. Unlike traditional models used water-related research, data-driven based can efficiently solve more complex nonlinear problems. In water environment conclusions derived from have been applied to construction, monitoring, simulation, evaluation, optimization various treatment management systems. Additionally, provide solutions pollution control, quality improvement, watershed ecosystem security management. this review, we describe cases which algorithms evaluate different environments, such as surface water, groundwater, drinking sewage, seawater. Furthermore, propose possible future applications approaches environments.

Language: Английский

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Factors Affecting Synthetic Dye Adsorption; Desorption Studies: A Review of Results from the Last Five Years (2017–2021)

Molecules, Journal Year: 2021, Volume and Issue: 26(17), P. 5419 - 5419

Published: Sept. 6, 2021

The primary, most obvious parameter indicating water quality is the color of water. Not only can it be aesthetically disturbing, but also an indicator contamination. Clean, high-quality a valuable, essential asset. Of available technologies for removing dyes, adsorption used method due to its ease use, cost-effectiveness, and high efficiency. process influenced by several parameters, which are basis all laboratories researching optimum conditions. main objective this review provide up-to-date information on studied influencing factors. effects initial dye concentration, pH, adsorbent dosage, particle size temperature illustrated through examples from last five years (2017-2021) research. Moreover, general trends drawn based these findings. removal time ranged 5 min 36 h (E = 100% was achieved within 5-60 min). In addition, nearly 80% efficiency with just 0.05 g adsorbent. It important reduce (with Φ decrease E 8-99%). Among dyes analyzed in paper, Methylene Blue, Congo Red, Malachite Green, Crystal Violet were frequently studied. Our conclusions previously published literature.

Language: Английский

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

Journal of environmental chemical engineering, Journal Year: 2020, Volume and Issue: 9(1), P. 104599 - 104599

Published: Oct. 18, 2020

Language: Английский

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An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions

Chemosphere, Journal Year: 2021, Volume and Issue: 277, P. 130126 - 130126

Published: March 19, 2021

Language: Английский

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Groundwater level prediction using machine learning models: A comprehensive review

Neurocomputing, Journal Year: 2022, Volume and Issue: 489, P. 271 - 308

Published: March 14, 2022

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential enhancing the planning and management of water resources. Over past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have published, reporting advances this field up to 2018. However, existing do not cover several aspects simulations ML, which are scientists practitioners working hydrology resource management. The current article aims provide a clear understanding state-of-the-art ML models implemented modeling milestones achieved domain. includes all types employed from 2008 2020 (138 articles) summarizes details reviewed papers, including models, data span, time scale, input output parameters, performance criteria used, best identified. Furthermore, recommendations possible future research directions improve accuracy enhance related knowledge outlined.

Language: Английский

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Prediction of groundwater quality using efficient machine learning technique

Chemosphere, Journal Year: 2021, Volume and Issue: 276, P. 130265 - 130265

Published: March 18, 2021

Language: Английский

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Groundwater quality forecasting using machine learning algorithms for irrigation purposes

Agricultural Water Management, Journal Year: 2020, Volume and Issue: 245, P. 106625 - 106625

Published: Nov. 7, 2020

Language: Английский

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The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities

Sustainability, Journal Year: 2020, Volume and Issue: 12(20), P. 8548 - 8548

Published: Oct. 15, 2020

The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics behaviors commonly associated with human intelligence. Today, various applications being used areas ranging from marketing to banking finance, agriculture healthcare security, space exploration robotics transport, chatbots creativity manufacturing. More recently, have also started become an integral part many urban services. Urban intelligences manage transport systems cities, run restaurants shops where every day urbanity expressed, repair infrastructure, govern multiple domains such as traffic, air quality monitoring, garbage collection, energy. In age uncertainty complexity that upon us, adoption expected continue, so its impact on sustainability our cities. This viewpoint explores questions lens smart sustainable generates insights into emerging potential symbiosis between urbanism. terms methodology, this deploys thorough review current status cities literature, research, developments, trends, applications. doing, it contributes existing academic debates fields AI. addition, by shedding light uptake seeks help policymakers, planners, citizens make informed decisions about

Language: Английский

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Source, fate, transport and modelling of selected emerging contaminants in the aquatic environment: Current status and future perspectives

Water Research, Journal Year: 2022, Volume and Issue: 217, P. 118418 - 118418

Published: April 7, 2022

Language: Английский

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

Applied Water Science, Journal Year: 2021, Volume and Issue: 11(12)

Published: Nov. 6, 2021

Abstract Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a index (WQI) requires some parameters. Conventionally, WQI computation consumes time and often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), subspace (RSS), additive (AR), neural network (ANN), support vector (SVR), locally weighted linear (LWLR), were employed generate prediction in Illizi region, southeast Algeria. Using best subset regression, 12 different input combinations developed strategy work was based on two scenarios. The first scenario aims reduce consumption computation, where all parameters used as inputs. second intends show variation critical cases when necessary analyses are unavailable, whereas inputs reduced sensitivity analysis. models appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root square (RMSE), relative (RAE), (RRSE). results reveal that TDS TH key drivers influencing study area. comparison performance evaluation metric shows MLR model has higher accuracy compared other terms 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, 3.1708*10–08% for R, MAE, RMSE, RAE, RRSE, respectively. executed less rate by RF 0.9984, 1.9942, 3.2488, 4.693, 5.9642 outcomes paper would be interest planners improving sustainable management plans groundwater resources.

Language: Английский

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