2022
DOI: 10.3390/rs14246229
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Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Abstract: 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… Show more

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Cited by 29 publications
(4 citation statements)
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“…Non-deterministic methods, including both statistical and machine learning approaches, are useful for developing predictive solutions in classification. Statistical methods like the analytical hierarchy process (AHP) [10], logistic regression (LR) [11], and frequency ratio (FR) [12], as well as machine learning methods like support vector machines (SVMs) [13], random forest (RFs) [14], and convolutional neural networks (CNNs) [15], are widely utilized for this purpose.…”
Section: Map Generation Techniquesmentioning
confidence: 99%
“…Non-deterministic methods, including both statistical and machine learning approaches, are useful for developing predictive solutions in classification. Statistical methods like the analytical hierarchy process (AHP) [10], logistic regression (LR) [11], and frequency ratio (FR) [12], as well as machine learning methods like support vector machines (SVMs) [13], random forest (RFs) [14], and convolutional neural networks (CNNs) [15], are widely utilized for this purpose.…”
Section: Map Generation Techniquesmentioning
confidence: 99%
“…Numerous machine learning models are employed to delineate flood-prone areas and assess the health of tea leaves, facilitating proactive management [35][36][37]. Simultaneously, artificial intelligence systems forecast alternative crops based on soil and climate data, aiming to optimize land utilization and minimize associated risks [32][33][34] Not specified in the provided information [27] Not specified Review of evapotranspiration approaches and concepts.…”
Section: Machine Learning In Agricultural Growthmentioning
confidence: 99%
“…It is appropriate to use the watershed technique to investigate different processes occurring at the surface of the ground, which is an area of the ground where the primary discharge is transferred to a single exit (Pande and Moharir 2017;Pande et al 2020). Watersheds, or hydrological units, are regarded to be more efficient and appropriate for conducting necessary surveys and investigations, as well as planning and implementing various improvement initiatives including water and soil conservation, and assuring their long-term viability (Singha et al 2022;Poongodi and Venkateswaran 2018;Shekar and Mathew 2022a;Krishnan et al 2017). Therefore, watershed management should be given special attention to address water-related issues Gautam et al 2023).…”
Section: Introductionmentioning
confidence: 99%