Flood and Landslide Prediction with Machine Learning

Introduction Floods and landslides rank most among the devastating natural hazards, making accurate prediction critical for minimizing their impacts. Traditional forecasting methods often face Issues based on complex nature of environmental systems. Recently, Machine Learning (ML) techniques have shown notable potential, enhancing predictive accuracy by utilizing big data, sophisticated algorithms, and real-time observations. This paper evaluates the latest progress in ML-based prediction systems for floods and landslides, highlighting popular models, available datasets, and existing challenges. Recently, machine learning (ML) techniques have shown notable potential, enhancing predictive accuracy by utilizing big data, sophisticated algorithms, and real- time observations.

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-05-07
DOI
https://doi.org/10.55041/ijsrem47193
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.00
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article

Flood and Landslide Prediction with Machine Learning

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Landslides and related hazards
article

Flood and Landslide Prediction with Machine Learning

article en

Abstract

Introduction Floods and landslides rank most among the devastating natural hazards, making accurate prediction critical for minimizing their impacts. Traditional forecasting methods often face Issues based on complex nature of environmental systems. Recently, Machine Learning (ML) techniques have shown notable potential, enhancing predictive accuracy by utilizing big data, sophisticated algorithms, and real-time observations. This paper evaluates the latest progress in ML-based prediction systems for floods and landslides, highlighting popular models, available datasets, and existing challenges. Recently, machine learning (ML) techniques have shown notable potential, enhancing predictive accuracy by utilizing big data, sophisticated algorithms, and real- time observations.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(05)
Openalex Percentile: Top 86%
Landslides and related hazards
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