Abstract

The research aims to propose the new ensemble models by combining the machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbour (KNN), boosted regression tree (BRT), and logitboost (LB) with the base classifier adabag (AB) for flood susceptibility mapping (FSM). The proposed models were implemented in the central west coast of India, which is vulnerable to flood events. For flood inventory mapping, a total of 210 flood localities were identified. Twelve effective factors were selected using the boruta algorithm for FSM. The area under the receiver operating characteristics (AUROC) curve and other statistical measures (sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and mean absolute error (MAE)) were employed to estimate and compare the success rate of the approaches. The validation results of the individual models in terms of AUC value were AB (92.74%) >RF (91.50%) >BRT (90.75%) >LB (89.07%) >NSC (88.97%) >KNN (83.88%), whereas the ensemble models showed that the AB-RF (94%) was of the highest prediction efficiency followed by, AB-KNN (93.33%), AB-NSC (93.02%), AB-LB (92.83%), and AB-BRT (92.64%). The outcomes of the ensemble models established that the AB is more appropriate to increase the accuracy of different single models. Therefore, this study can be useful for proper planning and management of the study area and flood hazard mapping in alike geographic environment.

摘要

该研究旨在通过结合机器学习技术提出新的集成模型，如旋转森林（RF）、最近收缩质心（NSC）、k-最近邻（KNN）、增强回归树（BRT）和logitboost（LB） ) 与用于洪水敏感性映射 (FSM) 的基分类器 adabag (AB)。所提出的模型在印度中部西海岸实施，该地区易受洪水事件的影响。对于洪水清单绘图，共确定了 210 个洪水地点。使用 FSM 的 boruta 算法选择了 12 个有效因素。采用接收者操作特征 (AUROC) 曲线下面积和其他统计量度（灵敏度、特异性、准确度、kappa、均方根误差 (RMSE) 和平均绝对误差 (MAE)）来估计和比较成功率方法。各个模型的AUC值验证结果为AB（92.74%）>RF（91.50%）>BRT（90.75%）>LB（89.07%）>NSC（88.97%）>KNN（83.88%），而集成模型显示，AB-RF（94%）的预测效率最高，其次是 AB-KNN（93.33%）、AB-NSC（93.02%）、AB-LB（92.83%）和 AB-BRT (92.64%)。集成模型的结果表明 AB 更适合提高不同单个模型的准确性。因此，本研究可用于研究区域的适当规划和管理以及相似地理环境中的洪水灾害测绘。而集成模型显示 AB-RF (94%) 的预测效率最高，其次是 AB-KNN (93.33%)、AB-NSC (93.02%)、AB-LB (92.83%) 和 AB-快速公交（92.64%）。集成模型的结果表明 AB 更适合提高不同单个模型的准确性。因此，本研究可用于研究区域的适当规划和管理以及相似地理环境中的洪水灾害测绘。而集成模型显示 AB-RF (94%) 的预测效率最高，其次是 AB-KNN (93.33%)、AB-NSC (93.02%)、AB-LB (92.83%) 和 AB-快速公交（92.64%）。集成模型的结果表明 AB 更适合提高不同单个模型的准确性。因此，本研究可用于研究区域的适当规划和管理以及相似地理环境中的洪水灾害测绘。