Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer

Mia, Md. Uzzal; Chowdhury, Tahmida Naher; Chakrabortty, Rabin; Pal, Subodh Chandra; Khalid, Muhammad; Costache, Romulus; Islam, Abu Reza Md. Towfiqul

doi:10.3390/land12040810

Cited by 9 publications

(2 citation statements)

References 128 publications

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“…NB is an important algorithm in the field of ML and data mining [61], applied to various fields, and is based on the Bayes probability theorem [70], which is suited for when the data have a high dimension and is not affected by the distribution of the data [71]. NB is a classifier with absolute independence assumptions between attributes [70]. The NB classification process within a set of factors affecting the prior probability of an MM occurrence can be expressed as follows:…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques—Case Study: Northern Lima Commonwealth, Peru

Badillo-Rivera,

Olcese,

Santiago

et al. 2024

Geosciences

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This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). A previous exploration of the topographic variables revealed a high correlation and multicollinearity among some of them, which led to dimensionality reduction through a principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being El Niño phenomenon and the second being an earthquake exceeding 8.8 Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of >0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8 Mw, approximately 40% and 35% respectively, of the NLC area would be exposed to the highest hazard levels. The importance of integrating methodologies in mass movement susceptibility models is also emphasized; these methodologies include the correlation analysis, multicollinearity assessment, dimensionality reduction of variables, and coupling statistical models with machine learning models to improve the predictive accuracy of machine learning models. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.

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Section: Naive Bayes (Nb)mentioning

confidence: 99%

A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques—Case Study: Northern Lima Commonwealth, Peru

Badillo-Rivera,

Olcese,

Santiago

et al. 2024

Geosciences

View full text Add to dashboard Cite

show abstract

“…NB is an important algorithm in the field of ML and data mining [40], applied to various fields, and is based on the Bayes probability theorem, Bayes rule, or Bayes formula [45], which is suitable when the data has a high dimension, is not affected by the distribution of the data, and all variables are considered independent of each other [46]. The main objective of NB is to determine the a priori probability of an event based on the proportion of observed cases relative to a specific output class [47].…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru

Badillo-Rivera,

Olcese Huerta,

Santiago Chirinos

et al. 2024

Preprint

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This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). Previous exploration of the topographic variables revealed high correlation and multicollinearity among some of them, which led to dimensionality reduction through principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being an El Niño phenomenon and the second being an earthquake exceeding 8.8Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of &gt;0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8Mw, approximately 40% of the NLC area would be exposed to the highest hazard levels. It also highlights the importance of integrating methodologies to reduce model uncertainty, such as comprehensive analysis of variables supported by high quality data and organized workflows. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.

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Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

Bammou,

Benzougagh,

Igmoullan

et al. 2024

Nat Hazards

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This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

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Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer

Cited by 9 publications

References 128 publications

A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques—Case Study: Northern Lima Commonwealth, Peru

A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques—Case Study: Northern Lima Commonwealth, Peru

A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

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