An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines.

By Bahram Choubin, E. Moradi, M. Golshan, Jan Adamowski, F. Sajedi-Hosseini, Amir Mosavi. Science of the Total Environment, 2019

688 citations recorded by the source corpus.

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b3f36128116c035202f863547f197d2cf835a0b0

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “References [224,226] compared the performances of ANFIS, ANNs, and SVM for the monthly prediction of ﬂoods.”
Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms (2021, background): “The assessment of FS is significantly dependent on the distance to river factor [52].”