Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

被引:15
|
作者
Gharakhanlou, Navid Mahdizadeh [1 ]
Perez, Liliana [1 ]
机构
[1] Univ Montreal, Dept Geog, Lab Environm Geosimulat LEDGE, 1375 Ave Therese Lavoie Roux, Montreal, PQ H2V 0B3, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
climate change; machine learning (ML); geographical information systems (GIS); flood susceptibility mapping; natural hazards; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; INTELLIGENCE APPROACH; FREQUENCY RATIO; CATCHMENT; REGRESSION; IMPACT; VALIDATION; MANAGEMENT; SELECTION;
D O I
10.3390/e24111630
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naive Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.
引用
收藏
页数:32
相关论文
共 50 条
  • [31] Modeling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India
    Satish Kumar Saini
    Susanta Mahato
    Deep Narayan Pandey
    Pawan Kumar Joshi
    Environmental Science and Pollution Research, 2023, 30 : 97463 - 97485
  • [32] Modeling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India
    Saini, Satish Kumar
    Mahato, Susanta
    Pandey, Deep Narayan
    Joshi, Pawan Kumar
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (43) : 97463 - 97485
  • [33] A Comparison of Machine Learning Models for Predicting Flood Susceptibility Based on the Enhanced NHAND Method
    Meng, Caisu
    Jin, Hailiang
    SUSTAINABILITY, 2023, 15 (20)
  • [34] How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region
    Saha, Tamal Kanti
    Pal, Swades
    Talukdar, Swapan
    Debanshi, Sandipta
    Khatun, Rumki
    Singha, Pankaj
    Mandal, Indrajit
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2021, 297 (297)
  • [35] Spatial Mapping of Flood Susceptibility Using Decision Tree-Based Machine Learning Models for the Vembanad Lake System in Kerala, India
    Sundar, Parthasarathy Kulithalai Shiyam
    Kundapura, Subrahmanya
    JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2023, 149 (10)
  • [36] A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems
    Amiri, Afshin
    Soltani, Keyvan
    Ebtehaj, Isa
    Bonakdari, Hossein
    JOURNAL OF HYDROLOGY, 2024, 632
  • [37] A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka
    Kurugama, Kumudu Madhawa
    Kazama, So
    Hiraga, Yusuke
    Samarasuriya, Chaminda
    JOURNAL OF FLOOD RISK MANAGEMENT, 2024, 17 (02):
  • [38] Flood Susceptibility Map of Periyar River Basin Using Geo-spatial Technology and Machine Learning Approach
    Sreekala S.
    Geetha P.
    Madhu D.
    Remote Sensing in Earth Systems Sciences, 2025, 8 (1) : 1 - 21
  • [39] Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling
    Panahi, Mahdi
    Khosravi, Khabat
    Rezaie, Fatemeh
    Kalantari, Zahra
    Lee, Jeong-A.
    JOURNAL OF HYDROLOGY-REGIONAL STUDIES, 2025, 58
  • [40] Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt
    Bosy A. El-Haddad
    Ahmed M. Youssef
    Hamid R. Pourghasemi
    Biswajeet Pradhan
    Abdel-Hamid El-Shater
    Mohamed H. El-Khashab
    Natural Hazards, 2021, 105 : 83 - 114