---
title: Machine Learning Models for Rainfall-Induced Landslide Prediction
url: https://www.ml-quant.com/papers/repec/spr-nathaz-v-120-y-2024-i-7-d-10-1007-s11069-024-06405-7/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:spr:nathaz:v:120:y:2024:i:7:d:10.1007_s11069-024-06405-7
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11069-024-06405-7%3Bh%3Drepec%3Aspr%3Anathaz%3Av%3A120%3Ay%3A2024%3Ai%3A7%3Ad%3A10.1007_s11069-024-06405-7
featured: 2024-05-28
citations: unknown
topic: ML & AI Methods
---


# Machine Learning Models for Rainfall-Induced Landslide Prediction

The study finds logistic regression models more effective than random forest models in predicting rainfall-induced landslides in India, especially when using antecedent precipitation data.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11069-024-06405-7%3Bh%3Drepec%3Aspr%3Anathaz%3Av%3A120%3Ay%3A2024%3Ai%3A7%3Ad%3A10.1007_s11069-024-06405-7
- Identifier: RePEc:spr:nathaz:v:120:y:2024:i:7:d:10.1007_s11069-024-06405-7
- Released: 2024-05-28
- First featured: Quant Letter No. 51 (2024-05-28): https://www.ml-quant.com/issues/2024-05-28/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Quantile Regression using Random Forest Proximities](https://www.ml-quant.com/papers/arxiv/2408.02355/): The article introduces a new method for calculating quantile regressions from random forests, showing improved performance and efficiency in predicting the average daily volume of corporate bonds.
- [Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals](https://www.ml-quant.com/papers/arxiv/2408.06679/): The study explores Explainable Case-Based Reasoning methods to clarify the results of black-box machine learning algorithms, using a technique to extract the learned distance metric from Random Forests, and assesses their explanatory power.
- [Enhanced Local Explainability and Trust Scores with Random Forest Proximities](https://www.ml-quant.com/papers/arxiv/2310.12428/): The study introduces a new method to explain the performance of random forest models by viewing them as adaptive weighted K nearest-neighbors models, offering a localized understanding of model predictions.
- [Financial Fraud Detection System Based on Improved Random Forest and Gradient Boosting Machine (GBM)](https://www.ml-quant.com/papers/arxiv/2502.15822/): The paper suggests a financial fraud detection system that uses an improved Random Forest and Gradient Boosting Machine model, offering an efficient and reliable solution for detecting financial fraud.
- [Intelligent beneficiary selection for social programs](https://www.ml-quant.com/papers/repec/ids-injdan-v-16-y-2024-i-2-p-181-206/): A prediction model using the random forest-based machine learning algorithm can accurately identify beneficiaries for social safety programs, improving traditional manual systems.
- [Software estimation with statistical models](https://www.ml-quant.com/papers/repec/gam-jmathe-v-12-y-2024-i-7-p-1058-d-1368511/): Machine learning techniques, particularly Random Forest, can accurately predict software development time and effort, reducing the risk of miscalculations.