Flood hazard risk assessment model based on random forest
By Zhaoli Wang, Chengguang Lai, Xiaohong Chen, Bing Yang, Shiwei Zhao, Xiaoyan Bai. 2015
710 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/a8f5a688920214d5531a8b507d6f1206ee710f48
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
- Omid Rahmati
- Hamid Darabi
- A. Torabi Haghighi
- Biswajeet Pradhan
- Hamid Reza Pourghasemi
- D. Tien Bui
- Binh Thai Pham
- Indra Prakash
In Management, Monitoring, Policy and Law
- Omid Rahmati
- Hamid Darabi
- A. Torabi Haghighi
- Biswajeet Pradhan
- D. Tien Bui
- Hamid Reza Pourghasemi
- Binh Thai Pham
- Indra Prakash
Citations in the corpus
- Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques (2019, background): “In addition, flatlands or lowlands have gentle slopes that reflect a constant threat of flooding (Wang et al., 2015; Rahmati et al., 2016; Pirnia et al., 2018; Torabi Haghighi et al., 2018).”
- Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, background): “In addition, RF is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values [9] [54].”