Enhancing flood prediction in Southern West Bengal, India using ensemble machine learning models optimized with symbiotic organisms search algorithm

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-03-22 DOI:10.1007/s00477-024-02712-4

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Abstract

In regions with limited flow and catchment data needed for the configuration and calibration of hydraulic and hydrological models, employing spatial flood modeling and mapping enables authorities to predict the spatial extent and severity of floods. This study leveraged flood inventory data, coupled with various conditional variables, to formulate a novel Ensemble model. This ensemble model combined four hybridized models based on Support Vector Machine (SVM), Naïve Bayes (NB), Decision Classification Tree (DCT), and Artificial Neural Network (ANN), all of which were optimized using the metaheuristic Symbiotic Organisms Search algorithm (SOS). The precision of the flood inundation map generated by the four hybrid models and the ensemble model was assessed using standard metrics. The results demonstrated that the ensemble model outperformed other models, with an accuracy metric of 0.99 Area Under the Curve (AUC) during the training stage and 0.96 during the testing stage. This underscores the effectiveness of the ensemble approach in flood preparedness and response applications. Furthermore, a comparison was conducted, comparing the performance of the developed ensemble model against other studies within the state of West Bengal. The findings highlighted a significant improvement in the ensemble model's performance with an AUC score of 0.96 in validation compared to studies in similar areas within West Bengal with AUC score ranged from 0.73 to 0.92. In conclusion, the methodology employed in this study holds promise for application in other regions worldwide that face challenges related to limited data availability for accurate flood inundation mapping.

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利用共生有机体搜索算法优化的集合机器学习模型加强印度西孟加拉邦南部的洪水预测

摘要在配置和校准水力和水文模型所需的流量和集水区数据有限的地区，采用空间洪水建模和绘图可使当局预测洪水的空间范围和严重程度。本研究利用洪水清单数据和各种条件变量，建立了一个新颖的集合模型。该集合模型结合了基于支持向量机（SVM）、奈夫贝叶斯（NB）、决策分类树（DCT）和人工神经网络（ANN）的四个混合模型，所有模型均使用元启发式共生体搜索算法（SOS）进行了优化。使用标准指标评估了四个混合模型和集合模型生成的洪水淹没图的精度。结果表明，集合模型的精度指标优于其他模型，在训练阶段曲线下面积（AUC）为 0.99，在测试阶段为 0.96。这凸显了集合方法在洪水防备和响应应用中的有效性。此外，还进行了一项比较，将所开发的集合模型的性能与西孟加拉邦内的其他研究进行了比较。研究结果表明，与西孟加拉邦类似地区的研究（AUC 得分为 0.73 至 0.92）相比，该集合模型的性能有了明显改善，在验证中的 AUC 得分为 0.96。总之，本研究采用的方法有望应用于全球其他地区，因为这些地区在绘制准确的洪水淹没地图时面临着数据可用性有限的挑战。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.