Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria

Omeka, Michael E.; Igwe, Ogbonnaya; Onwuka, O. S.; Nwodo, Ogechukwu M; Ugar, Samuel I; Undiandeye, Peter A; Anyanwu, Ifeanyi Emmanuel

doi:10.1007/s11356-023-25291-3

Cited by 17 publications

(8 citation statements)

References 71 publications

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“…An ANN model's architectural framework is made up of three fundamental layers: input, hidden, and output. Using complex mathematical functions and knowledge of the different underlying patterns associated with a dataset, the multiple layers can join to process and create useful responses as a function of the incoming data (Kouadri et al 2021;Omeka et al 2023). Errors may occur during a normal data input process; however, ANNs may mitigate for this flaw due to their high accuracy in quantitative analysis.…”

Section: Modeling Of Artificial Neural Networkmentioning

confidence: 99%

“…Additionally, lower modeling errors were observed from the results of RE and SOSE, further affirming the performance of ANN2 over ANN1. This variation in performance between the two models could be attributed to variations in output activation functions and optimization algorithms (Egbueri et al 2023;Omeka et al 2023).…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

“…In southeastern Nigeria, the detachability and liquefaction potential index (DLPI) have been predicted by integrating MLR and MLP-NN to forecast the erodibility potential of tropical soils (Egbueri et al 2023). This model has also been successfully applied in combination with the analytical hierarchy process (AHP) for irrigation water quality prioritization and prediction in southeastern Nigeria (Omeka et al 2023). From our literature review, no study has been carried out on the prediction of potentially toxic elements (PTEs) in soil by predicting the pollution load index (PLI).…”

Section: Introductionmentioning

confidence: 99%

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Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria

Omang

Omeka

Asinya

et al. 2023

Preprint

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Potentially toxic elements occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational mistakes. As a result, in order to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the full study region. Furthermore, the multilayer perceptron artificial neural network was used to forecast the most important toxic components influencing soil quality (MLP-NN). Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic components ingestion than skin exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing towards the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil

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Section: Modeling Of Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria

Omang

Omeka

Asinya

et al. 2023

Preprint

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“…Several techniques and methodologies have been employed for this objective, with positive outcomes in evaluating groundwater quality, delineating pollution risks, and assessing health hazards. These methodologies include index-based approaches, statistical methods, and Geographic Information System-based techniques, which are commonly used for groundwater quality assessment and mapping (Gaagai et al, 2023;Gao et al, 2020;Gebrehiwot et al, 2011;Howladar et al, 2018;Jesuraja et al, 2021;Omeka et al, 2023;Raheja et al, 2023b).…”

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

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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.

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“…As such, one model can certify particular water as suitable while the other may disagree, resulting in a bias in judgment and decision. Hence, for a holistic and unbiased water quality assessment, the use of the integrated approach is recommended for better decision-making (Egbueri et al, 2021;Omeka et al, 2023). Other health risk assessment indices have been based on only the children and adult population sizes and taking into account only the ingestion pathway (Adamu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Irrigation suitability and health risk assessment of groundwater resources in the Firozabad industrial area of north-central India: An integrated indexical, statistical, and geospatial approach

Saraswat

Nath

Omeka

et al. 2023

Front. Environ. Sci.

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The recent global upsurge in anthropogenic activities has resulted in a decline in the quality of water. This by extension has resulted in increased ubiquity of water pollution in terms of sources. The application of traditional water quality assessment methods usually involves the use of conventional water quality parameters and guideline values. This may be associated with bias and errors during the computation of various sub-indices. Hence, to overcome this limitation, it is critical to have a visual appraisal of the water quality in terms of source and human health risks exposure for sustainable water resource management and informed decision-making. Therefore, the present study has integrated multiple water quality assessment indices, spatio-temporal, and statistical models to assess the suitability of fifty groundwater samples (n = 50) within the Firozabad industrial area for irrigation and drinking; as well as the likely health risks from oral intake and dermal contact by inhabitants. Electrical conductivity (mean = 1,576.6 μs/cm), total hardness (mean = 230.9 mg/L), dissolved sodium (mean = 305.1 mg/L) chloride (mean = 306.1 mg/L) and fluoride (mean = 1.52 mg/L) occurred in the water at concentrations above the recommended standards; attributed influxes from agricultural and industrial wastewater. The pollution index of groundwater and water quality index revealed that 100% of the groundwater samples are extremely polluted; this was also supported by the joint multivariate statistical analyses. The majority of the irrigational water quality indices (sodium adsorption ratio, Kelly’s Ratio, permeability index, percent sodium) revealed that the long-term use of the groundwater for irrigation in the area will result in reduced crop yield unless remedial measures are put in place. Higher Hazard index (HI > 1) for nitrate and fluoride ingestion was recorded in water for the children population compared to adult; an indication that the children population is more predisposed to health risks from the oral intake of water. Generally, risk levels from ingestion appear to increase in the western and north-eastern parts of the study area. From the findings of this study, it is highly recommended that adequate agricultural practices, land use, and water treatment regulatory strategies be put in place for water quality sustainability for enhanced agricultural production and human health protection.

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Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria

Cited by 17 publications

References 71 publications

Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria

Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria

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

Irrigation suitability and health risk assessment of groundwater resources in the Firozabad industrial area of north-central India: An integrated indexical, statistical, and geospatial approach

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