View in EDS

A geological disaster risk prediction method using susceptibility and ensemble learning algorithms: direct economic loss prediction.

Saved in:
Bibliographic Details
Title: A geological disaster risk prediction method using susceptibility and ensemble learning algorithms: direct economic loss prediction.
Authors: He, Daixun1,2 (AUTHOR), Chen, Huayu1,2 (AUTHOR), Chen, Rui1,2 (AUTHOR) cechenrui@hit.edu.cn, Liu, Leilei1,2 (AUTHOR), Rattan, Bharat1,2 (AUTHOR), Liang, Zizhao1,2 (AUTHOR)
Source: Environmental Earth Sciences. May2026, Vol. 85 Issue 11, p1-18. 18p.
Subject Terms: *Ensemble learning, *Economic impact analysis, *Risk assessment, *Geology databases, *Landslide prediction, *Data integration
Geographic Terms: China
Abstract: Geological disasters have led to significant economic losses and casualties in South China. However, existing indicators for predicting geological disaster risk are inconsistent and generally lack physical meanings. This study aims to develop an intuitive and reliable geological disaster prediction method by using direct economic loss as the output indicator to establish a geological disaster economic loss prediction (GELP) model. First, multi-source data, including exposure, geological disaster susceptibility, and disaster-causing factors, are integrated to create a GELP database using data related to six types of geological disasters, i.e., landslides, rockfalls, debris flows, ground fissures, land subsidence, and ground collapse. Next, multiple GELP models are developed using three ensemble learning algorithms, including categorical boosting (CatBoost), Stacked Generalization (Stacking) and Extreme Gradient Boosting (XGBoost), and two single machine learning algorithms. The performance is evaluated using R-squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), whereas their reliability is validated through geological disaster cases. The results show that integrating multi-source data with ensemble machine learning models can produce a more reliable geological disaster economic loss prediction model, as the CatBoost model performs the best overall, achieving an R2 of 0.67. Additionally, the CatBoost model demonstrates high accuracy at a county scale. When the losses are smaller, the models' predictions are closer to the actual values. Furthermore, integrating historical data from multiple geological disasters not only enhances the models' applicability but also enlarges the scale of training datasets, allowing for more thorough training, which significantly improves performance. These findings offer valuable insights for developing reliable GELP models. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
FullText Text:
  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 194452078
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A geological disaster risk prediction method using susceptibility and ensemble learning algorithms: direct economic loss prediction.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22He%2C+Daixun%22">He, Daixun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Huayu%22">Chen, Huayu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Rui%22">Chen, Rui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> cechenrui@hit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Leilei%22">Liu, Leilei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rattan%2C+Bharat%22">Rattan, Bharat</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Zizhao%22">Liang, Zizhao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Environmental+Earth+Sciences%22">Environmental Earth Sciences</searchLink>. May2026, Vol. 85 Issue 11, p1-18. 18p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Economic+impact+analysis%22">Economic impact analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br />*<searchLink fieldCode="DE" term="%22Geology+databases%22">Geology databases</searchLink><br />*<searchLink fieldCode="DE" term="%22Landslide+prediction%22">Landslide prediction</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Geological disasters have led to significant economic losses and casualties in South China. However, existing indicators for predicting geological disaster risk are inconsistent and generally lack physical meanings. This study aims to develop an intuitive and reliable geological disaster prediction method by using direct economic loss as the output indicator to establish a geological disaster economic loss prediction (GELP) model. First, multi-source data, including exposure, geological disaster susceptibility, and disaster-causing factors, are integrated to create a GELP database using data related to six types of geological disasters, i.e., landslides, rockfalls, debris flows, ground fissures, land subsidence, and ground collapse. Next, multiple GELP models are developed using three ensemble learning algorithms, including categorical boosting (CatBoost), Stacked Generalization (Stacking) and Extreme Gradient Boosting (XGBoost), and two single machine learning algorithms. The performance is evaluated using R-squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), whereas their reliability is validated through geological disaster cases. The results show that integrating multi-source data with ensemble machine learning models can produce a more reliable geological disaster economic loss prediction model, as the CatBoost model performs the best overall, achieving an R2 of 0.67. Additionally, the CatBoost model demonstrates high accuracy at a county scale. When the losses are smaller, the models' predictions are closer to the actual values. Furthermore, integrating historical data from multiple geological disasters not only enhances the models' applicability but also enlarges the scale of training datasets, allowing for more thorough training, which significantly improves performance. These findings offer valuable insights for developing reliable GELP models. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194452078
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s12665-026-12970-w
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 1
    Subjects:
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Economic impact analysis
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Geology databases
        Type: general
      – SubjectFull: Landslide prediction
        Type: general
      – SubjectFull: Data integration
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: A geological disaster risk prediction method using susceptibility and ensemble learning algorithms: direct economic loss prediction.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: He, Daixun
      – PersonEntity:
          Name:
            NameFull: Chen, Huayu
      – PersonEntity:
          Name:
            NameFull: Chen, Rui
      – PersonEntity:
          Name:
            NameFull: Liu, Leilei
      – PersonEntity:
          Name:
            NameFull: Rattan, Bharat
      – PersonEntity:
          Name:
            NameFull: Liang, Zizhao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 25
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 18666280
          Numbering:
            – Type: volume
              Value: 85
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Environmental Earth Sciences
              Type: main
ResultId 1