A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

JOURNAL OF HYDROLOGY

卷:603

Article

Darabi, Hamid¹  Haghighi, Ali Torabi¹  Rahmati, Omid²  Shahrood, Abolfazl Jalali¹  Rouzbeh, Sajad³  Pradhan, Biswajeet^4,6,7  Bui, Dieu Tien⁵ 

[1] Univ Oulu, Water Energy & Environm Engn Res Unit, POB 4300, FIN-90014 Oulu, Finland

[2] AREEO, Kurdistan Agr & Nat Resources Res & Educ Ctr, Soil Conservat & Watershed Management Res Dept, Sanandaj, Iran

[3] Sari Agr Sci & Nat Resources Univ, Dept Watershed Management, POB 737, Sari, Iran

[4] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Adv Modeling & Geospatial Informat Syst CAMGI, Sydney, NSW 2007, Australia

[5] Univ South Eastern Norway, Dept Business & IT, GIS Grp, Gullbringvegen 36, N-3800 Bo Telemark, Norway

[6] Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea

[7] Univ Kebangsaan Malaysia, Inst Climate Change, Earth Observat Ctr, Ukm 43600, Bangi Selangor, Malaysia

关键词: Flood inundation;    Flood inventory;    GIS;    NN-SGW model;    Confusion matrix;   

DOI  :  10.1016/j.jhydrol.2021.126854

来源: Elsevier

【 摘 要 】

In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future studies seeking to predict flood susceptibility in urban areas using hybridized machine learning (ML) models and can also be applied in other urban areas where flood inundation presents a pressing challenge, and there are some problems regarding required model and availability of input data.

【 授权许可】

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