A novel hybrid artificial intelligence approach for flood susceptibility assessment
By Kamran Chapi, Vijay P. Singh, Ataollah Shirzadi, Himan Shahabi, D. Tien Bui, Binh Thai Pham, Khabat Khosravi. Environmental Modelling & Software, 2017
561 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/12a392788f2eeabdf4d8bbe1c1f43f53d83e1973
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In Global and Planetary Change
- Khabat Khosravi
- Binh Thai Pham
- Ataollah Shirzadi
- Himan Shahabi
- Baharin bin Ahmad
- D. Tien Bui
- N. Al‐Ansari
- Omid Rahmati
In Water Science and Technology
Citations in the corpus
- Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, methodology): “Although ANFIS is a powerful algorithm and has higher predictive power than both ANN and FL, its membership function fails to adequately determine optimum weights [34,35], hence an optimization algorithm has been applied to calculate optimum values automatically [8,9,36,37].”
- A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers (2020, background): “96), there is significant differences between the performance of the models [72,85].”