Comparative Analysis of Various Machine Learning Techniques for Flood Prediction

Abraham, Sajimon; R, Jyothish V; Thomas, Sijo; Jose, Benymol

doi:10.1109/icitiit54346.2022.9744177

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…The second measure was the F1 score, which combines the precision and recall scores of a classifier [78]. The formula for the F1 score is as follows:…”

Section: F1 Scorementioning

confidence: 99%

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

Li,

Woodley,

Chappell

2024

Sustainability

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Throughout history, natural disasters have caused severe damage to people and properties worldwide. Flooding is one of the most disastrous types of natural disasters. A key feature of flood assessment has been making use of the information derived from remote-sensing imagery from optical sensors on satellites using spectral indices. Here, a study was conducted about a recent spectral index, the Normalised Difference Inundation Index, and a new ensemble spectral index, the Concatenated Normalised Difference Water Index, and two mature spectral indices: Normalised Difference Water Index and the differential Normalised Difference Water Index with four different machine learning algorithms: Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbours applied to the PlanetScope satellite imagery about the Brisbane February 2022 flood which is in urban environment. Statistical analysis was applied to evaluate the results. Overall, the four algorithms provided no significant difference in terms of accuracy and F1 score. However, there were significant differences when some variations in the indices and the algorithms were combined. This research provides a validation of existing measures to identify floods in an urban environment that can help to improve sustainable development.

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“…The second measure was the F1 score, which combines the precision and recall scores of a classifier [78]. The formula for the F1 score is as follows:…”

Section: F1 Scorementioning

confidence: 99%

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

Li,

Woodley,

Chappell

2024

Sustainability

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An Ensemble Machine Learning Approach for Predicting Flood Based on Meteorological and Topographical Features: A Comparative Study in Kalu Ganga River Basin, Sri Lanka

Mahaganapathy,

Jayasinghe,

Rathnayaka

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Cloud-based disaster management architecture using hybrid machine learning approach in IoT

Özen,

Souri

2024

Multimed Tools Appl

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Natural disasters are becoming more frequent and more severe as a result of global warming. It is critical to take precautions before disasters, to gather and analyze information simultaneously while they are happening, and to make accurate assessments after them given that the deaths and injuries brought on by such disasters both leave lasting traumas in the life of society and damage the economy. Internet of Things (IoT) technology, is a young field that can assist intelligent safety-critical systems with data collection, processing in cloud edge data centers, and application of prediction methodologies for discovering key points and unexpected patterns using 5G technology. With the use of a cloud-based prediction algorithm for disaster management in the IoT environment, this study seeks to quickly process the data that is gathered during disasters and to speed up the analysis that will be done both during and after the disasters. An Optimized Ensemble Bagged Tree (OEBT) algorithm with ANOVA-based feature selection is developed for this aim. The experimental results show that accuracy, F1-Score, precision, and recall of the proposed OEBT algorithm utilizing the US Natural Disasters Dataset are 97.9%, 78.3%, 98.7%, and 78.9%, respectively. Comparisons with decision tree, logistic regression, and the traditional ensemble techniques are made. The suggested algorithm outperforms them all in terms of success rates.

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Comparative Analysis of Various Machine Learning Techniques for Flood Prediction

Cited by 7 publications

References 9 publications

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

An Ensemble Machine Learning Approach for Predicting Flood Based on Meteorological and Topographical Features: A Comparative Study in Kalu Ganga River Basin, Sri Lanka

Cloud-based disaster management architecture using hybrid machine learning approach in IoT

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