Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions.

中文翻译：

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通过使用频率比，k近邻和随机森林算法通过洪水传播项目适宜性制图进行水资源管理

在许多国家，尤其是在中东，水资源短缺是一个普遍的问题。洪水蔓延项目（FSP）是一种人工补给技术，通常建议用于干旱和半干旱地区，其主要目标包括两个：（1）缓解洪水和（2）人工补给地下水。这项研究实施了三种最新的流行模型，包括频率比（FR），k近邻（KNN）和随机森林（RF）来确定土地是否适合FSP。第一步，根据国家指南和文献确定适合FSP的区域。然后，通过多次实地调查对确定的区域进行验证。要生成FSP土地适宜性图，需要考虑几个FSP条件因素，例如地形（即坡度，平面曲率和剖面曲率），水文地质（即透射率，含水层厚度和电导率），水文（即降雨，与河流的距离，河流密度）和渗透率），岩性和土地利用作为模型的输入。对于FR建模，使用上述变量的分类层，而在KNN和RF算法中实现它们的连续层。在最后一步，接收器工作特性（ROC）曲线用于评估所应用算法的能力和准确性。根据调查结果，RF，KNN和FR模型的ROC曲线下面积分别为97.1％，94.6％和89.2％。此外，在建模过程中，透射率，坡度，含水层厚度，与河流的距离，降雨和电导率被认为是影响最大的因素。这项研究的结果表明，可以建议使用RF，KNN和FR来确定在其他地区建立FSP的合适区域。在建模过程中，坡度，含水层厚度，与河流的距离，降雨和电导率被认为是影响最大的因素。这项研究的结果表明，可以建议使用RF，KNN和FR来确定在其他地区建立FSP的合适区域。在建模过程中，坡度，含水层厚度，与河流的距离，降雨和电导率被认为是影响最大的因素。这项研究的结果表明，可以建议使用RF，KNN和FR来确定在其他地区建立FSP的合适区域。