Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction

Saber, Mohamed; Boulmaiz, Tayeb; Guermoui, Mawloud; Abdrado, Karim I.; Kantoush, Sameh A.; Takemon, Yasuhiro; Boutaghane, Hamouda; Nohara, Daisuke; Mabrouk, Emad

doi:10.1080/10106049.2021.1974959

Cited by 81 publications

(23 citation statements)

References 131 publications

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“…Saber et al used LGBM and CatBoost model to predict the ash ood susceptibility of Hurghada, Egypt. The results showed that the AUC values of the two models were higher than 97%, with high prediction accuracy, and LGBM model was superior to CatBoost model in classi cation index and processing time (Saber et al 2021). The above research shows that LGBM model not only has a high level of predictive ability, but also has a faster training speed.…”

Section: Introductionmentioning

confidence: 76%

Comparative study on landslide susceptibility of different evaluation units based on LightGBM-SHAP

Sun

Wen

et al. 2023

Preprint

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Landslide is a common natural disaster, which has a serious impact on human life, property safety and socioeconomic development. Landslide susceptibility zoning can predict the spatial distribution of landslide occurrence probability. Based on grid units, slope units and terrain units, this study explore the influence of different evaluation units on regional landslide susceptibility zoning. Taking Yunyang County as a case study, 15 influencing factors such as elevation, slope and curvature were selected to establish a geospatial database, and the light gradient boosting machine (LGBM) algorithm was used to const-ruct the landslide susceptibility model (LSM). The results show that the accuracy of LSM constructed by different evaluation units is diffe-rent. Among them, the LGBM model based on grid units has the highest accuracy, with an accuracy of 0.7589, F1-Score of 0.7453, and the area under curve (AUC) values in training data set and verification data set were 0.8998and 0.8099, respectively. In addition, SHaply Additive ExPlanation (SHAP) is used to explain the model. The global interpretation shows that elevation, distance from river and distance from road have great influence on landslide in the study area. Local interpretation found that elevation, distance from the river and distance from the road have a greater impact on Jiuxianping landslide. This study can provide scientific reference for LSM construction and disaster prevention.

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Section: Introductionmentioning

confidence: 76%

Comparative study on landslide susceptibility of different evaluation units based on LightGBM-SHAP

Sun

Wen

et al. 2023

Preprint

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“…This algorithm differs from the other algorithms in the growth of the tree in-depth or by leaves. LGBM handles large amounts of data with the lowest memory requirements 33 , 34 . Almost all the modern gradient-based methods work well with numerical attributes.…”

Section: Methodsmentioning

confidence: 99%

A machine learning approach for predicting suicidal ideation in post stroke patients

Song

Hong

Lee

et al. 2022

Sci Rep

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Currently, the identification of stroke patients with an increased suicide risk is mainly based on self‐report questionnaires, and this method suffers from a lack of objectivity. This study developed and validated a suicide ideation (SI) prediction model using clinical data and identified SI predictors. Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data were collected from October 2012 to March 2014. The data were then applied to three boosting models (Xgboost, CatBoost, and LGBM) to identify the comparative and best performing models. Demographic variables that showed significant differences between the two groups were age, onset, type, socioeconomic, and education level. Additionally, functional variables also showed a significant difference with regard to ADL and emotion (p < 0.05). The CatBoost model (0.900) showed higher performance than the other two models; and depression, anxiety, self-efficacy, and rehabilitation motivation were found to have high importance. Negative emotions such as depression and anxiety showed a positive relationship with SI and rehabilitation motivation and self-efficacy displayed an inverse relationship with SI. Machine learning-based SI models could augment SI prevention by helping rehabilitation and medical professionals identify high-risk stroke patients in need of SI prevention intervention.

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“…Sahin [88] found that CatBoost model is superior in predicting landslide susceptibility areas in the Bolu region of Turkey. Similarly, Saber et al [89] also appreciated CatBoost and LightGBM algorithms in fash food susceptibility. Zhou et al [56] proposed a fre prediction model using the CatBoost algorithm for Yunnan Province in China.…”

Section: Evaluation and Comparison Of The Wildfre Susceptibilitymentioning

confidence: 97%

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Abujayyab

Kassem

Khan

et al. 2022

Advances in Civil Engineering

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Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.

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Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction

Cited by 81 publications

References 131 publications

Comparative study on landslide susceptibility of different evaluation units based on LightGBM-SHAP

Comparative study on landslide susceptibility of different evaluation units based on LightGBM-SHAP

A machine learning approach for predicting suicidal ideation in post stroke patients

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

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