Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

By Hossein Shafizadeh-Moghadam, Roozbeh Valavi, Himan Shahabi, Kamran Chapi, Ataollah Shirzadi. Journal of Environmental Management, 2018

336 citations recorded by the source corpus.

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/133830b4951015348ce7cc26f44ca32475176d7b

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

  1. Ataollah Shirzadi
  2. Baharin bin Ahmad
  3. Khabat Khosravi
  4. Himan Shahabi
  5. N. Al‐Ansari
  6. Marten Geertsema
  7. John J. Clague
  8. Assefa M. Melesse

In Management, Monitoring, Policy and Law

  1. Ataollah Shirzadi
  2. Baharin bin Ahmad
  3. Himan Shahabi
  4. Marten Geertsema
  5. Khabat Khosravi
  6. John J. Clague
  7. Omid Rahmati
  8. Ebrahim Omidvar

Citations in the corpus