Casting light on forcing and breaching scenarios that lead to marine inundation: Combining numerical simulations with a random-forest classification approach. (June 2018)

• Record Type: Journal Article
• Authors: Rohmer, J.; Idier, D.; Paris, F.; Pedreros, R.; Louisor, J.
• Is Part Of: Environmental modelling & software. Volume 104(2018)
• Page Start: 64
• Page End: 80
• Publication Date: 2018-06
• Subjects: Coastal flooding -- Overflow -- Random forest -- Classification probability -- Variable importance analysis -- Partial dependence plot; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.03.003
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Identifying the offshore forcing and breaching conditions that lead to marine inundation is of high importance for risk management. This task cannot be conducted by using a numerical hydrodynamic model due to its high computation time cost (of several minutes or even hours). In the present study, we show how the random forest (RF) classification technique can approximate the numerical model to explore these critical conditions. We focus on the Bouchôleurs site, which is located on the French Atlantic coast and exposed to overflow processes. An iterative strategy is developed for selecting the numerical simulations (a total of 200) to train the RF model. The sensitivity to the input parameters is studied using permutation-based importance measures and extended versions of the partial dependence plots. The results highlight the key interplay among the high-tide level, the surge peak and the phase difference, and the complex role of the breaching location. Highlights: Offshore forcing and breaching conditions leading to flooding are identified. Random Forest classification method is used for exploring these critical scenarios. Variable importance is analysed using the Boruta algorithm. Classification rules are explored using Individual Conditional Expectation Plots. The approach is applied to the real case of Bouchôleurs in France.