Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms

被引:18
|
作者
Ahmed, Ishita Afreen [1 ]
Talukdar, Swapan [1 ]
Shahfahad [1 ]
Parvez, Ayesha [2 ]
Rihan, Mohd [1 ]
Baig, Mirza Razi Imam [1 ]
Rahman, Atiqur [1 ]
机构
[1] Jamia Millia Islamia, Dept Geog, Fac Nat Sci, New Delhi, India
[2] Univ Calif Irvine, Henry Samueli Sch Engn, Dept Elect Engn & Comp Sci, Irvine, CA USA
关键词
Flood susceptibility mapping; urban watershed; metaheuristic optimization algorithms; particle swarm optimization; machine learning algorithms; FUZZY INFERENCE SYSTEM; LAND USE/LAND COVER; WEIGHTS-OF-EVIDENCE; STATISTICAL-MODELS; PARTICLE SWARM; SURFACE RUNOFF; AREA; OPTIMIZATION; TREES;
D O I
10.1080/10106049.2022.2066200
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The urban watershed of Guwahati is a highly flood-prone region and the fastest growing city situated on the bank of the Brahmaputra River. Therefore, this study aims to the urban flood susceptibility mapping of Guwahati city using metaheuristic optimization algorithms integrated with random forest (RF) machine learning algorithm. Further, the receiver operating characteristic (ROC) and multiple error measurements were applied to analyze the performances of the models used. The result showed that about one-third of the area of Guwahati city is under the high and very high flood risk while nearly 50% area comes under low and very low flood risk. The value of the area under curve (AUC) of ROC was above 0.80 for all the integrated models applied. However, the RF-bee colony (BCO) and the RF-based ant colony (ACO) are the two best flood susceptibility models that performed better in the analysis. The methodology adopted in the study is cost and time effective and can be used for the flood susceptibility modeling in other parts of the world. Further, the findings of this study can useful in the flood mitigation and planning process.
引用
收藏
页码:12238 / 12266
页数:29
相关论文
共 50 条
  • [21] A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment
    Habibi, Alireza
    Delavar, Mahmoud Reza
    Sadeghian, Mohammad Sadegh
    Nazari, Borzoo
    Pirasteh, Saeid
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 122
  • [22] Geospatial modeling using hybrid machine learning approach for flood susceptibility
    Bibhu Prasad Mishra
    Dillip Kumar Ghose
    Deba Prakash Satapathy
    Earth Science Informatics, 2022, 15 : 2619 - 2636
  • [23] Geospatial modeling using hybrid machine learning approach for flood susceptibility
    Mishra, Bibhu Prasad
    Ghose, Dillip Kumar
    Satapathy, Deba Prakash
    EARTH SCIENCE INFORMATICS, 2022, 15 (04) : 2619 - 2636
  • [24] Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery
    Razavi-Termeh, Seyed Vahid
    Sadeghi-Niaraki, Abolghasem
    Seo, MyoungBae
    Choi, Soo-Mi
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 873
  • [25] Flash flood susceptibility mapping based on catchments using an improved Blending machine learning approach
    Yin, Yongqiang
    Zhang, Xiaoxiang
    Guan, Zheng
    Chen, Yuehong
    Liu, Changjun
    Yang, Tao
    HYDROLOGY RESEARCH, 2023, 54 (04): : 557 - 579
  • [26] Remotely sensed desertification modeling using ensemble of machine learning algorithms
    Boali, Abdolhossein
    Asgari, Hamid Reza
    Behbahani, Ali Mohammadian
    Salmanmahiny, Abdolrassoul
    Naimi, Babak
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2024, 34
  • [27] Investigating the efficacy of physics-based metaheuristic algorithms in combination with explainable ensemble machine-learning models for landslide susceptibility mapping
    Razavi-Termeh, Seyed Vahid
    Sadeghi-Niaraki, Abolghasem
    Naqvi, Rizwan Ali
    Choi, Soo-Mi
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2025, 39 (03) : 1109 - 1141
  • [28] Assessment of urban flood susceptibility based on a novel integrated machine learning method
    Yang, Haidong
    Zou, Ting
    Liu, Biyu
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2024, 197 (01)
  • [29] Improved tree-based machine learning algorithms combining with bagging strategy for landslide susceptibility modeling
    Tingyu Zhang
    Renata Pacheco Quevedo
    Huanyuan Wang
    Quan Fu
    Dan Luo
    Tao Wang
    Guilherme Garcia de Oliveira
    Laurindo Antonio Guasselli
    Camilo Daleles Renno
    Arabian Journal of Geosciences, 2022, 15 (2)
  • [30] Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt
    Ahmed M. Youssef
    Hamid Reza Pourghasemi
    Bosy A. El-Haddad
    Environmental Science and Pollution Research, 2022, 29 : 66768 - 66792