Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR DOI Creative Commons
Sackdavong Mangkhaseum, Yogesh Bhattarai, Sunil Duwal

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 28, 2024

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human natural ecosystems in Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing machine learning techniques is proposed to address this issue enhancing flood susceptibility understanding informed decision-making. This study utilizes geo-datasets algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, Long Short-Term Memory) generate comprehensive maps. The results highlight Random Forest's superior performance, achieving highest train test Area Under Curve Receiver Operating Characteristic (AUROC) (1.00 0.993), accuracy (0.957), F1-score (0.962), kappa value (0.914), with lowest mean squared error (0.207) Root Mean Squared Error (0.043). Vulnerability particularly pronounced low-elevation low-slope southern downstream areas (Central part PDR). reveal that 36%–53% basin's total area highly susceptible flooding, emphasizing dire need for coordinated floodplain management strategies. research uses freely accessible data, addresses data scarcity studies, provides valuable insights disaster risk sustainable planning

Language: Английский

Ensemble machine learning paradigms in hydrology: A review DOI
Mohammad Zounemat‐Kermani, Okke Batelaan, Marzieh Fadaee

et al.

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 598, P. 126266 - 126266

Published: April 1, 2021

Language: Английский

Citations

450
A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping DOI Creative Commons
Quoc Bao Pham, Yacine Achour, Sk Ajim Ali

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2021, Volume and Issue: 12(1), P. 1741 - 1777

Published: Jan. 1, 2021

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in Kysuca river basin, Slovakia. For this reason, previous were analyzed with 16 conditioning factors. Landslide inventory was divided into training (70% of locations) validating dataset (30% locations). heuristic approach Fuzzy Decision Making Trial Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes (NBC) Extreme Gradient Boosting (XGBoost), respectively. results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% 40.3% total basin area had very high LS corresponding FDEMATEL-ANP, FR, LR, RFC, NBC XGBoost model, analysis revealed RFC most accurate model (overall accuracy 98.3% AUC 97.0%). Besides, FDEMATEL-ANP 93.8% 92.4%) better prediction capability than FR 86.9% 86.1%), LR 90.5% 91.2%), machine learning 76.3% 90.9%) even deep 92.3% 87.1%) models. outweighed models, suggests methods should be tested out before directly applying

Language: Английский

Citations

115
Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations DOI
Halit Enes Aydin, Muzaffer Can İban

Natural Hazards, Journal Year: 2022, Volume and Issue: 116(3), P. 2957 - 2991

Published: Dec. 20, 2022

Language: Английский

Citations

89
Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches DOI Creative Commons
Khalifa M. Al‐Kindi, Zahra Alabri

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 8(1), P. 63 - 81

Published: Jan. 1, 2024

Abstract This study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random forest (RF), and CatBoost (CB)—applying them to meticulously curated datasets topographical, geological, environmental parameters; goal was investigate intricacies flood susceptibility within arid riverbeds Wilayat As-Suwayq, which is situated in Sultanate Oman. The results underscored exceptional discrimination prowess XGB CB, boasting impressive area under curve (AUC) scores 0.98 0.91, respectively, during testing phase. RF, a stalwart contender, performed commendably with an AUC 0.90. Notably, investigation revealed that certain key variables, including curvature, elevation, slope, stream power index (SPI), topographic wetness (TWI), roughness (TRI), normalised difference vegetation (NDVI), were critical achieving accurate delineation flood-prone locales. In contrast, ancillary factors, such as annual precipitation, drainage density, proximity transportation networks, soil composition, geological attributes, though non-negligible, exerted relatively lesser influence on susceptibility. empirical validation further corroborated by robust consensus XGB, RF CB models. By amalgamating advanced deep techniques precision geographical information systems (GIS) rich troves remote-sensing data, can be seen pioneering endeavour realm analysis cartographic representation semiarid fluvial landscapes. findings advance our comprehension vulnerability dynamics provide indispensable insights for development proactive mitigation strategies regions are susceptible hydrological perils.

Language: Английский

Citations

21
Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis DOI Creative Commons
Hoang Thi Hang, Javed Mallick, Saeed Alqadhi

et al.

Environmental Technology & Innovation, Journal Year: 2024, Volume and Issue: 35, P. 103655 - 103655

Published: May 5, 2024

Forest fires pose a significant threat to ecosystems and socio-economic activities, necessitating the development of accurate predictive models for effective management mitigation. In this study, we present novel machine learning approach combined with Explainable Artificial Intelligence (XAI) techniques predict forest fire susceptibility in Nainital district. Our innovative methodology integrates several robust — AdaBoost, Gradient Boosting Machine (GBM), XGBoost Random Deep Neural Network (DNN) as meta-model stacking framework. This not only utilises individual strengths these models, but also improves overall prediction performance reliability. By using XAI techniques, particular SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-agnostic Explanations), improve interpretability provide insights into decision-making processes. results show effectiveness ensemble model categorising different zones: very low, moderate, high high. particular, identified extensive areas susceptibility, precision, recall F1 values underpinning their effectiveness. These achieved ROC AUC above 0.90, performing exceptionally well an 0.94. The are remarkably inclusion confidence intervals most important metrics all emphasises robustness reliability supports practical use management. Through summary plots, analyze global variable importance, revealing annual rainfall Evapotranspiration (ET) key factors influencing susceptibility. Local analysis consistently highlights importance rainfall, ET, distance from roads across models. study fills research gap by providing comprehensive interpretable modelling that our ability effectively manage risk is consistent environmental protection sustainable goals.

Language: Английский

Citations

20
Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods DOI
Hüseyın Akay

Soft Computing, Journal Year: 2021, Volume and Issue: 25(14), P. 9325 - 9346

Published: May 26, 2021

Language: Английский

Citations

104
Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill, India DOI
Subham Roy, Arghadeep Bose, Indrajit Roy Chowdhury

et al.

Arabian Journal of Geosciences, Journal Year: 2021, Volume and Issue: 14(11)

Published: May 27, 2021

Language: Английский

Citations

84
Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm DOI

Nguyễn Thị Thùy Linh,

Manish Pandey, Saeid Janizadeh

et al.

Advances in Space Research, Journal Year: 2022, Volume and Issue: 69(9), P. 3301 - 3318

Published: Feb. 22, 2022

Language: Английский

Citations

56
Advancing flood warning procedures in ungauged basins with machine learning DOI Creative Commons
Zimeena Rasheed, Akshay Aravamudan, Ali Gorji Sefidmazgi

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127736 - 127736

Published: March 17, 2022

Language: Английский

Citations

49
Parameters and methods used in flood susceptibility mapping: a review DOI Creative Commons
Çağla Melisa KAYA, Leyla Derin

Journal of Water and Climate Change, Journal Year: 2023, Volume and Issue: 14(6), P. 1935 - 1960

Published: May 11, 2023

Abstract A correct understanding of the parameters and methods used in flood susceptibility mapping (FSM) is critical for identifying strengths limitations different approaches, as well developing methodologies. In this study, we examined scientific publications literature using WoS. Although number quite high, these varies, with a maximum 21 minimum 5 preferred. It was found that most commonly parameter has preference rate 97%, but there no common 100% studies. The determining include multi-criteria decision-making (MCDM) methods, physically based hydrological models, statistical various soft computing methods. use traditional MCDM already high among researchers, analysis have evolved over years from human judgments to on big data machine learning. reviewed studies, it observed learning, fuzzy logic, metaheuristic optimization algorithms, heuristic search which are been widely FSM recent years.

Language: Английский

Citations

38