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Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda.

Martin Kuradusenge¹, Santhi Kumaran², Marco Zennaro³

¹African Centre of Excellence in Internet of Things, University of Rwanda, Kigali 3900, Rwanda.

International Journal of Environmental Research and Public Health

|June 14, 2020

Summary

This summary is machine-generated.

This study introduces improved landslide prediction models using machine learning techniques (MLT), specifically random forest (RF) and logistic regression (LR). Incorporating antecedent rainfall data significantly enhances prediction accuracy and reduces false negatives for early warning systems.

Keywords:

antecedent rainfall landslide logistic regression prediction rainfall random forest

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An R-Based Landscape Validation of a Competing Risk Model

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Area of Science:

Geosciences and Environmental Science
Computational Science and Machine Learning

Background:

Landslides are unpredictable natural disasters causing significant destruction.
Existing early warning systems using Internet of Things (IoT) or basic machine learning techniques (MLT) have limitations in prediction accuracy.
Accurate landslide prediction is crucial for timely alerts and life-saving measures.

Purpose of the Study:

To propose and evaluate two advanced prediction modeling approaches: random forest (RF) and logistic regression (LR).
To improve landslide prediction accuracy by incorporating antecedent cumulative rainfall data alongside other environmental and internal parameters.
To compare the performance of RF and LR models using metrics like ROC-AUC and False Negative Rate (FNR).

Main Methods:

Development and application of random forest (RF) and logistic regression (LR) models for landslide prediction.
Utilized rainfall datasets, internal landslide-related parameters (e.g., slope angle), and antecedent cumulative rainfall data.
Model performance evaluation using Receiver Operating Characteristic Area Under the Curve (ROC-AUC) and False Negative Rate (FNR).

Main Results:

Both RF and LR models showed significantly improved performance with antecedent rainfall data, achieving AUCs of 0.995 and 0.997, respectively.
A strong correlation was found between antecedent precipitation and landslide occurrence, outperforming one-day rainfall predictions.
The logistic regression (LR) model demonstrated superior performance with a lower FNR (3.84% with antecedent rainfall) compared to the RF model.

Conclusions:

Antecedent cumulative rainfall is a critical factor for improving landslide prediction accuracy.
The logistic regression (LR) model, particularly when enhanced with antecedent rainfall data, is highly effective for landslide prediction and early warning systems.
Slope angle was identified as the most impactful internal factor among those analyzed.