Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.

洪水空间敏感性预测是制定洪水缓解策略和减少洪水破坏的第一步。洪水发生是一个复杂的过程，很难通过简单的方法进行预测。这项研究描述了使用元优化算法（包括蚱optimization优化算法（GOA）和粒子群优化（PSO））的支持向量回归（SVR）的优化，用于伊朗加兹温平原的洪水建模。得出了包括9个易于获得的地质环境洪水调节因素（即地面坡度，坡向，高程，平面曲率，剖面曲率，河道附近，土地利用，岩性和降雨）的地理空间数据。信息增益比（IGR）方法用于确定输入变量的相对重要性。根据现有报告创建了43个位置的历史洪水清单地图。地理空间数据和历史洪水位被用于构建训练和测试数据集。然后，使用经过训练的数据集，使用带有GOA和PSO算法的优化SVR模型，生成洪水敏感性图。最后，使用均方根误差（RMSE），平均绝对误差（MAE）和接收器工作特征曲线（ ROC）曲线下面积（AUC）的统计量度对模型的预测准确性进行量化。尽管GOA和PSO算法都改善了SVR性能，但SVR-GOA模型的效果最佳（AUC  = 0.959，RMSE  = 0.31和MSE  = 0.098），然后是SVR-PSO模型（AUC  = 0.959，RMSE  = 0.33和MSE  = 0.11）和独立的SVR模型（AUC  = 0.87，RMSE  = 0.35和MSE  = 0.12）。高程，岩性和坡向具有最高的IGR值，被确定为洪水敏感性的最有效预测因子。