Evolutionary and ensemble machine learning predictive models for evaluation of water quality

Aldrees, Ali; Javed, Muhammad Faisal; Taha, Abubakr Taha Bakheit; Mohamed, Abdeliazim Mustafa; Jasiński, Michał; Gono, Miroslava

doi:10.1016/j.ejrh.2023.101331

Cited by 12 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, three machine learning algorithms, including Support vector machine (SVM), Random forest (RF), and Extreme Gradient Boosting (XGBoost), are used to predict the IWQI. However, the extensive literature on machine learning algorithms used can be found in (Aldrees et al, 2023;K. Chen et al, 2020;T.…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

“…Kouadri et al, (2022) used three ML models such as long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN) to forecast the groundwater quality for irrigation in the Maharashtra region of India. Another study by Aldrees et al, (2023) used Multi-expression programming (MEP) and Random forest (RF) regression models to predict water quality in the Indus River basin of Pakistan. Singha et al, (2021) used XGBoost, RF, and ANN models for groundwater quality assessment in the Raipur district of India.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Raheja,

Goel,

Pal

2024

Preprint

View full text Add to dashboard Cite

The main objective of the present study is to evaluate the groundwater quality for irrigation purposes in the central-western part of Haryana state (India). For this, 272 groundwater samples were collected during the Pre- and Post-monsoon periods in 2022. Several indices, including Sodium Absorption Ratio (SAR), Permeability Index (PI), Sodium Percentage (Na %), Kelly Ratio (KR), Magnesium Adsorption Ratio (MAR), and Irrigating water quality index (IWQI) were derived. The results in terms of SAR, Na%, and KR values indicate that the groundwater is generally suitable for irrigation. On the other hand, PI and MAR exceeded the established limits, primarily showing issues related to salinity and magnesium content in the groundwater. Furthermore, according to the groundwater quality assessment based on the IWQI classification, 47.06% and 25% of the total collected samples fell under the "Severe Restriction for irrigation" category during the Pre-monsoon and Post-monsoon periods, respectively. Spatial variation maps indicate that water quality in the western portion of the study area is unsuitable for irrigation during both periods. Three Machine learning (ML) algorithms, namely Random forest (RF), Support vector machine (SVM), and Extreme Gradient Boosting (XGBoost) were integrated and validated to predict the IWQI. The results revealed that the XGBoost with Random searchachieves the best prediction performances. The approaches established in this study have been confirmed to be cost-effective and feasible for groundwater quality, using hydrochemical parameters as input variables, and highly beneficial for water resource planning and management.

show abstract

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Raheja,

Goel,

Pal

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Since our campaign included seasonal samplings at 7 and 12 sampling stations of the Laspias and Lissos rivers, respectively, this methodology could be applied with certainty. Equation (1) shows the formula of the CCME-WQI.…”

Section: Assessment Based On the Ccme-wqimentioning

confidence: 99%

“…Surface water is the main freshwater resource of the world [1] and is a requirement for the welfare of all forms of life and an essential condition of ecological diversity and sustainable development [2,3]. Rivers, as the primary source of freshwater, render an irreplaceable service for human survival and social development.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, river quality deterioration has raised significant concerns from governments and the public due to the increasing urbanization and rapid growth of the economy [4,5]. Studies have shown that river water quality is mainly affected by the excessive discharge of agricultural drainage [6], as well as from industrial and urban outfalls [1,7]. As a result, environmental protection is rising as an important issue in public policy throughout the word, and the developing countries face a serious threat associated with the lack of appropriate integrated management of aquatic ecosystems and the challenges posed by climate change [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Spring to Coast Approach in Small-Scale Catchments and Adjacent Coastal Zone

Papaevangelou,

Bakalakou,

Ntislidou

et al. 2024

Water

View full text Add to dashboard Cite

Surface waters, particularly rivers, are paramount in serving as the primary global water source and a pivotal economic driver. Various pollution sources can negatively impact water quality. The Water Framework Directive has established regulations that define specific chemical and ecological statuses for rivers. Consequently, there is an ongoing commitment to monitor their quality closely. This study involved the collection of samples from two watersheds (Laspias and Lissos) within the Eastern Macedonia and Thrace Region. The two rivers flow along regions characterized by notable environmental stressors, including WWTP, landfills, industrial zones, and agricultural areas, which also constitute substantial contributors to the local economy. This study’s outcomes, covering from springs to coast, are presented and analyzed using various indices, including the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), discriminate analysis, and the trophic status index (TRIX). Water quality assessment included the measurement of physicochemical parameters, common pollutants, and major ions. The analysis revealed “bad” water quality status along most of the Laspias and in specific sectors of the Lissos, with parameters exceeding the thresholds set by legislation. The rivers demonstrated significant organic and nutrient pollution. Given that water quality in these rivers is significantly influenced by urban, agricultural, and industrial runoff, the imperative need for change necessitates interventions to improve water quality. Observations and measurements are fundamental prerequisites for raising awareness among citizens and stakeholders and for finding effective management measures for the two river basins.

show abstract

Study of biological quality of lake waters based on phycocyanin using tree‐based methodologies

Kheimi,

Almadani,

Ramezani‐Charmahineh

et al. 2024

Ecohydrology

View full text Add to dashboard Cite

The provision of drinking water, agricultural, and industrial applications by reservoirs has made lake exploration and monitoring unavoidable. The features of the ecosystem, particularly physical and chemical elements, influence the evaluation of the quality of water resources. Lakes undergo extensive qualitative changes due to their vast amount of water. In general, these bodies of water represent geological conditions as well as water contamination produced by natural and human activities. In the present research, the prediction of the amount of phycocyanin (fPC) in the water of Lake Michigan has been implemented employing four tree‐based machine learning techniques based on seasonality factors. Phycocyanin has significant effects on quality parameters such as turbidity, chlorophyll concentration, algal bloom, and dissolved oxygen in water by affecting the photosynthesis process of algae. Therefore, in this study, the prediction of the amount of phycocyanin dissolved in the lake water using the mentioned variables, along with the temperature of the water, specific conductance, and pH, has been able to interpret the quality of the water and the occurrence of phenomena such as algal blooms. The results of the models in predicting fPCs equal to 0.44 and 0.55 μg/L were consistent with the natural conditions of the lake, and it seems that ensemble tree–based models, along with the biological index of fPC, formed the right combination of input and output parameters in modeling and obtained the lowest prediction error (root‐mean‐square error [RMSE] boosted trees = 0.0140 and RMSE random forests = 0.0141 μg/L).

show abstract

Evolutionary and ensemble machine learning predictive models for evaluation of water quality

Cited by 12 publications

References 60 publications

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

The Spring to Coast Approach in Small-Scale Catchments and Adjacent Coastal Zone

Study of biological quality of lake waters based on phycocyanin using tree‐based methodologies

Contact Info

Product

Resources

About