Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

Harsa, Hastuadi; Hidayat, Anistia Malinda; Mulsandi, Adi; Suprihadi, Bambang; Kurniawan, Roni; Habibie, Muhammad Najib; Hutapea, T D; Swarinoto, Yunus S.; Makmur, Erwin Eka Syahputra; Fitria, Welly; Sudewi, Rahayu Sapta Sri; Praja, Alfan Sukmana

doi:10.11591/ijai.v12.i1.pp262-270

Cited by 3 publications

(2 citation statements)

References 19 publications

(19 reference statements)

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“…Moreover, Machine Learning can play a pivotal role in expediting and enhancing the accuracy of decisionmaking during emergency scenarios. By leveraging Machine Learning algorithms, computer systems possess the capability to analyze real-time data derived from diverse sources, including weather and fire sensors, as well as social media platforms, thereby furnishing pertinent and precise information to authorities for the purpose of implementing effective mitigation strategies [13]. In summation, Machine Learning harbors considerable potential in supporting the mitigation of non-structural disasters.…”

Section: Methodsmentioning

confidence: 99%

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Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Fallo,

Nipu

2024

BAREKENG: J. Math. & App.

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This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. An evaluation of model performance, anchored in accuracy and Kappa metrics, unveiled that both the Ordinal Logistic Regression and Random Forest models exhibited noteworthy precision, reaching a commendable 74.36%. Nevertheless, a meticulous examination of Kappa values disclosed the ascendancy of the Random Forest model, which achieved a superior Kappa value of 0.5397. As portrayed in the visual representation provided, it becomes manifest that the Random Forest algorithm's prognostications yield 66 instances of cloudy atmospheric conditions, 48 occurrences of light precipitation, and 3 episodes of moderate rainfall. These predictions are influenced by several factors, including average temperature, humidity levels, wind speed, duration of sunlight, and wind direction at maximum speed. Consequently, this comprehensive analysis underscores the Random Forest algorithm as the most efficacious model for landslide susceptibility prediction. Furthermore, the study seamlessly integrated overlay maps, encompassing the Slope Inclination Map of the Takari District, Geological Map of the Takari District, and Soil Type Map of the Takari District, to contribute to the formulation of a definitive map delineating the susceptibility to landslides in the Takari District. Furthermore, further research could conduct spatial validation of the model predictions using additional datasets or remote sensing data to validate the accuracy of the landslide susceptibility map and ensure its applicability across different geographical regions.

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Section: Methodsmentioning

confidence: 99%

“…Random Forest is a machine learning algorithm that combines multiple decision trees to make predictions. Each tree is built on a random subset of the training data and features, and the algorithm selects the best split points based on certain criteria [13]. The final prediction is made by aggregating the predictions from all the trees.…”

Section: Random Forestmentioning

confidence: 99%

Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Fallo,

Nipu

2024

BAREKENG: J. Math. & App.

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Rainfall-induced landslide prediction models, part ii: deterministic physical and phenomenologically models

Ebrahim,

Gomaa,

Zayed

et al. 2024

Bull Eng Geol Environ

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Landslides are frequent hillslope events that may present significant risks to humans and infrastructure. Researchers have made ongoing efforts to assess the potential danger associated with landslides, intending to ascertain the location, frequency, and magnitude of these events in a given area. This study is meant to supplement the previous study (Part I), which explored empirical and physically based causative thresholds. In this paper (Part II), a systematic review is used to conduct an in-depth study of existing research on prediction models. Deterministic physical approaches were investigated for local-scale landslides. Next, national-scale landslide susceptibility models are discussed, including qualitative and quantitative models. Consequently, key findings about rainfall-induced landslides are reviewed. The strategy selection is generally governed by data and input factors from a macroscopic perspective, while the better prediction model is defined by dataset quality and analysis model performance from a microscopic perspective. Physically based causative thresholds can be used with limited geotechnical or hydrological data; otherwise, numerical analysis provides optimal accuracy. Among all statistical models, the hybrid artificial intelligence model achieved the best accuracy. Finally, current challenges have concentrated on integrating AI and physical models to obtain high accuracy with little data, prompting research suggestions. Advanced constitutive models for real-time situations are lacking. Dynamic and spatiotemporal susceptibility maps are also used, although their subjectivity needs further research. This study analyses how to choose the best model and determine its key traits. This research provides valuable insights for scholars and practitioners seeking innovative approaches to lessen the severity of landslides.

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Optimizing nailing parameters for hybrid retaining systems using supervised learning regression models

Menon,

Kolathayar

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

Cited by 3 publications

References 19 publications

Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Implementation of Mapping-Based Machine Learning Algorithm as Non-Structural Disaster Mitigation to Detect Landslide Susceptibility in Takari District

Rainfall-induced landslide prediction models, part ii: deterministic physical and phenomenologically models

Optimizing nailing parameters for hybrid retaining systems using supervised learning regression models

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