Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Baye...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Switzerland), 2022-12, Vol.14 (24), p.6229
Hauptverfasser:	Singha, Chiranjit, Swain, Kishore Chandra, Meliho, Modeste, Abdo, Hazem Ghassan, Almohamad, Hussein, Al-Mutiry, Motirh
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Anthropogenic factors Artificial intelligence AUROC Bayesian analysis Boruta techniques Decision trees Emergency communications systems Flood control Flood damage Flood hazards Flood mapping flood susceptibility Floods Genetic algorithms Geomorphology Households Humidity hybrid machine learning Hydrology Land area Learning algorithms Machine learning Mathematical models National parks NDVI Precipitation Remote sensing Soil permeability Sound Spatial analysis Statistical models Storm damage Support vector machines Wind Zoning
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Beschreibung
Zusammenfassung:	Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (
ISSN:	2072-4292 2072-4292
DOI:	10.3390/rs14246229