Department of Geology, University of Calabar, P.M.B. 1115, Calabar, Cross River State, Nigeria.
Department of Geology, Faculty of Physical Sciences, University of Nigeria, Nsukka, Enugu State, Nigeria.
Environ Sci Pollut Res Int. 2024 Sep;31(41):54204-54233. doi: 10.1007/s11356-023-25291-3. Epub 2023 Feb 1.
Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO, Cl, Mg, and SO were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction.
农业生产力可能会因灌溉水质不佳而受损。因此，充分的脆弱性评估和确定对准确预测最有影响的水质参数对于加强水资源管理和可持续性至关重要。在这项研究中，地理信息系统（GIS）、层次分析法（AHP）技术和机器学习模型被整合起来，以评估和预测奥库鲁梅图伊-伊亚梅特农业矿区的灌溉水质（IWQ）适宜性。为了实现这一目标，根据其对作物产量的敏感性，将六个水质标准重新分类为四个主要危害组（渗透性和渗透性危害、盐度危害、特定离子毒性和混合效应）。使用 AHP 成对比较矩阵计算了标准的归一化权重。根据 IWQ 参数（电导率、总溶解固体、钠吸附比、渗透率指数、可溶性钠百分比、镁危害、硬度和 pH）生成了八个专题地图，并在 ArcGIS 环境中进行了栅格化，以使用加权和技术生成该区域的灌溉适宜性地图。得出的 IWQ 地图显示，该地区 28.2%的水适合灌溉，43.7%的水中度适宜，28.1%的水不适宜，灌溉水质在中东南方向恶化。两种机器学习模型-多层感知器神经网络（MLP-NN）和多元线性回归（MLR）被整合并验证来预测 IWQ 参数。MLR 和 MLP-NN 的决定系数（R）分别为 0.513 到 0.858 和 0.526 到 0.861。基于所有指标的结果，MLP-NN 的性能精度高于 MLR。从 MLP-NN 敏感性分析的结果来看，HCO、Cl、Mg 和 SO 被确定为对该地区灌溉水质影响最大的因素。本研究表明，GIS-AHP 和机器学习的集成可以作为灌溉水质监测和预测的高效快速决策工具。
