Flood susceptibility modeling by integrating tree‐based regression with metaheuristic algorithm, BWO

IF 2.1 3区 地球科学 Q2 GEOGRAPHY Transactions in GIS Pub Date : 2024-05-08 DOI:10.1111/tgis.13171
Deba Prakash Satapathy, Bibhu Prasad Mishra
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Abstract

Floods are becoming more widely acknowledged as a common occurrence of nature's dangers on a global scale. Although forecasting models primarily focus on timely warnings, models aimed at evaluating dangerous zones can play a vital role in shaping policies for adaptation, mitigation, and reducing the risk of disasters. Using machine learning techniques including hybrid black widow optimization (BWO) with XGBoost, LGBoost, and AdaBoost. We generate a flood susceptibility map for considered region of lower mahanadi basin (LMB). This study examines the effectiveness of these machine learning models in assessing and mapping flood susceptibility, while also providing suggestions for future research in this area. Flood susceptibility model was developed using 13 variables: Altitude, Aspect, Curvature, Distance from river, Drainage Density, Stream Power Index (SPI), Sediment Transport Index (STI), Rainfall intensity, Land Use Land Cover (LULC), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), Normalized Difference Vegetation Index (NDVI), and slope. Additionally, flood inventory data were incorporated into the model. Dataset was divided into a 70% portion for training model and a 30% portion for validating model. To assess the performance of the model, several evaluation metrics were employed, including receiver operating characteristic (ROC) curve and other performance indices. Evaluation of flood susceptibility mapping, using ROC curve method in combination with flood density yielded strong and reliable results for various models. BWO‐XGBoost achieved a score of 0.889, BWO‐LGBoost achieved a score of 0.937, and BWO‐ADABoost achieved a score of 0.904. These scores indicate effectiveness of these models in accurately predicting flood susceptibility in the study area. A comparison was made with commonly used methods in flood susceptibility assessment to evaluate the efficiency of proposed models. It was found that having a first‐class and enlightening database is crucial for accurately classifying flood types in flood susceptibility mapping. This aspect greatly contributes to improving the overall performance of the model. Among the evaluated methods, the hybrid model BWO‐LGBoost demonstrated better performance compared with others, indicating its effectiveness in accurately predicting flood susceptibility.
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通过将基于树的回归与元搜索算法相结合建立洪水易感性模型,BWO
人们越来越普遍地认识到,洪水是全球范围内常见的自然灾害。虽然预报模型主要侧重于及时预警,但旨在评估危险区域的模型可在制定适应、缓解和降低灾害风险的政策方面发挥重要作用。利用机器学习技术,包括混合黑寡妇优化(BWO)与 XGBoost、LGBoost 和 AdaBoost。我们生成了下马哈纳迪盆地(LMB)考虑区域的洪水易感性地图。本研究检验了这些机器学习模型在评估和绘制洪水易感性地图方面的有效性,同时也为该领域的未来研究提供了建议。洪水易感性模型是利用 13 个变量建立的:这些变量包括:海拔高度(Altitude)、坡度(Aspect)、曲率(Curvature)、与河流的距离(Distance from river)、排水密度(Drainage Density)、溪流动力指数(SPI)、沉积物迁移指数(STI)、降雨强度(Rainfall intensity)、土地利用土地覆盖(LULC)、地形湿润指数(TWI)、地形粗糙度指数(TRI)、归一化植被指数(NDVI)和坡度。此外,模型还纳入了洪水清单数据。数据集分为 70% 用于训练模型,30% 用于验证模型。为了评估模型的性能,采用了一些评估指标,包括接收者操作特征曲线(ROC)和其他性能指标。使用 ROC 曲线法结合洪水密度对洪水易感性绘图进行评估,各种模型都得出了可靠的结果。BWO-XGBoost 的得分为 0.889,BWO-LGBoost 的得分为 0.937,BWO-ADABoost 的得分为 0.904。这些得分表明,这些模型在准确预测研究区域洪水易感性方面非常有效。为了评估所提出模型的效率,我们将其与洪水易感性评估中常用的方法进行了比较。研究发现,在绘制洪水易发性地图时,拥有一流且具有启发性的数据库对于准确划分洪水类型至关重要。这在很大程度上有助于提高模型的整体性能。在所评估的方法中,BWO-LGBoost 混合模型与其他方法相比表现出更好的性能,表明其在准确预测洪水易感性方面的有效性。
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来源期刊
Transactions in GIS
Transactions in GIS GEOGRAPHY-
CiteScore
4.60
自引率
8.30%
发文量
116
期刊介绍: Transactions in GIS is an international journal which provides a forum for high quality, original research articles, review articles, short notes and book reviews that focus on: - practical and theoretical issues influencing the development of GIS - the collection, analysis, modelling, interpretation and display of spatial data within GIS - the connections between GIS and related technologies - new GIS applications which help to solve problems affecting the natural or built environments, or business
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