Novel evolutionary-optimized neural network for predicting landslide susceptibility

Adnan, Rana Muhammad; Khan, Imran; Moayedi, Hossein; Dehrashid, Atefeh Ahmadi; Elkhrachy, Ismail; Le, Binh Nguyen

doi:10.1007/s10668-023-03356-0

Cited by 20 publications

(4 citation statements)

References 114 publications

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“…These maps illustrate the "where" slope failure will occur in this region and predict "how frequently" it appears. This is a significant improvement of this study compared to previous studies that use a specific rainfall map for landslide susceptibility mapping (Adnan Ikram et al, 2023;Le et al, 2023;. However, the 5 years of time series rainfall data (2016-2020) applied in this paper is not long enough to make a long-term prediction.…”

Section: Discussionmentioning

confidence: 82%

“…The higher the consecutive days of maximum rainfall, the more occurrence of landslides. This approach is more reasonable than previous studies when only using an average annual rainfall map (Adnan Ikram et al, 2023;Le et al, 2023; or a specific cumulative rainfall map (Bui et al, 2012;Zhang et al, 2022) for landslide assessment.…”

Section: Discussionmentioning

confidence: 86%

“…However, this approach is less reasonable because this rainfall type cannot represent a triggering factor that causes landslide occurrences, especially in regions such as Vietnam, where slope failures occur in the rainy season. Therefore, rainfall does not accurately reflect its influence on landslide spatial prediction in previous studies (Adnan Ikram et al, 2023;Le et al, 2023;.…”

Section: Introductionmentioning

confidence: 90%

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Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Doan,

Nguyen,

Nguyen

et al. 2024

Vietnam J. Earth Sci.

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Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 90%

See 1 more Smart Citation

Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Doan,

Nguyen,

Nguyen

et al. 2024

Vietnam J. Earth Sci.

View full text Add to dashboard Cite

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“…This equation calculates the area under the receiver operating characteristic curve (ROC) (AUC), which reflects the sensitivity and specificity of continuous variables, with each point on the curve representing susceptibility to the same signal stimulus (Adnan Ikram et al., 2023; Chen et al., 2023). A higher AUC value indicates higher accuracy and more reliable prediction results from the corresponding model (Jaafari et al., 2017).…”

Section: Methodsmentioning

confidence: 99%

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

Wang,

Le,

Tian

et al. 2024

Earth and Space Science

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Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater.

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A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan

Ahmadi Dehrashid,

Dong,

Fatahizadeh

et al. 2024

Stoch Environ Res Risk Assess

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Novel evolutionary-optimized neural network for predicting landslide susceptibility

Cited by 20 publications

References 114 publications

Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan

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