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Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm

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Abstract

The Taihang Mountains in China span numerous cities, where landslide disasters occur frequently in the mountainous areas, jeopardizing the lives and properties of residents. Consequently, it is of great significance to focus on prevention and control of landslide disasters in the region. Currently, a single model is commonly employed to analyze landslide susceptibility mapping (LSM), but the accuracy of the results fails to meet the demands of early warning, prevention, and control. This paper focuses on the Taihang Mountain area as the research area, organizes the collection of landslide disaster potential points and related influence factor data, and employs the information quantity method to derive a composite machine learning model by coupling with Random Forest (RF) and Extreme Gradient Boosting (XGB), subsequently utilizing the Genetic Optimization Algorithm (GA) to optimize the model. The performance of the composite model is enhanced using the Genetic Algorithm (GA), employing accuracy, regression rate, precision, F1 score, AUC value, and Taylor diagram to evaluate the comprehensive accuracy of the model results, with a susceptibility map generated for comparative analysis. The results demonstrate that the IV-GA-RF model performs optimally (accuracy = 0.956, precision = 0.96, recall = 0.953, F1 score = 0.957, AUC = 0.946 for the testing set, AUC = 0.929 for the training set), with all-around improvement in performance metrics compared to the unoptimized composite model, with metric values improving by 0.044, 0.051, 0.046, 0.044, 0.021 and 0.020, respectively. The IV-GA-RF model exhibits a significant advantage over the IV-GA-XGB algorithm, also optimized using the GA algorithm. The accuracy of the susceptibility map produced by the IV-GA-RF model is superior, as assessed by the Seed Cell Area Index (SCAI) method. The four factors of slope, rainfall, seismicity, and stratigraphic lithology are crucial in determining the occurrence of landslides in the study area. In summary, the IV-GA-RF model can be utilized as an effective model for analyzing landslide disasters, providing a reference for research in this field and contributing scientific insights to disaster prevention and control efforts in the study area; simultaneously, the concept of the composite optimization model introduces new perspectives into this field.

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No datasets were generated or analysed during the current study.

References

  • Achu AL et al (2024) Redefining landslide susceptibility under extreme rainfall events using deep learning. Geomorphology 448

  • Aditian A et al (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon. Indonesia Geomorphology 318:101–111

    Article  Google Scholar 

  • Agboola G, Beni LH, Elbayoumi T, Thompson G (2024) Optimizing landslide susceptibility mapping using machine learning and geospatial techniques. Ecol Inf 81

  • Ageenko A et al (2022) Landslide susceptibility mapping using machine learning: a Danish case study. ISPRS International J Geo-Information 11(6)

  • Arabameri A, Pradhan, Biswajeet, Rezaei K, Lee C-W (2019) Assessment of landslide susceptibility using statistical-and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Rem Sens 11(9):999–999

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):73–81

    Article  Google Scholar 

  • Behnia P, Blais-Stevens A (2018) Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada. Nat Hazards 90:1407–1426

    Article  Google Scholar 

  • Berhane G, Kebede M, Alfarah N, Hagos E, Grum B, Giday A, AberaT (2020) Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial datasets in the Adwa-Adigrat Mountain chains, northern Ethiopia. Jou Afr Ear Sci 164:103795

    Article  Google Scholar 

  • Brahim LA, Bousta M, Jemmah IA et al (2018) Landslide susceptibility mapping using AHP method and GIS in the peninsula of Tangier (Rif-northern Morocco). MATEC Web of Conferences 149 (3–4), 02084

  • Brenning A (2008) Statistical geocomputing combining R and SAGA: the example of landslide susceptibility analysis with generalized additive models. SAGA—seconds out 19:23–32

    Google Scholar 

  • Chang TC, Chien YH (2007) The application of genetic algorithm in debris flows prediction. Environ Geol 53:339–347

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794)

  • Chen W, Xie X, Wang J, Pradhan B, Hong H, Tien Bui D, Duan Z, Ma J (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151:147–160

    Article  Google Scholar 

  • Chen W, Zhang S, Li R, Shahabi H (2018) Performance evaluation of the gis-based data mining techniques of best-first decision tree, random forest, and naive bayes tree for landslide susceptibility modeling. Sci Total Environ 644(dec10):1006–1018

    Article  CAS  Google Scholar 

  • Chen W, Panahi M, Tsangaratos P, Shahabi H, Ahmad BB (2019) Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. CATENA 172:212–231

    Article  Google Scholar 

  • Chowdhury MS, Rahman MN, Sheikh MS, Sayeid MA, Mahmud KH, Hafsa B (2024) Gis-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in chattogram district. Bangladesh Heliyon 10(1)

  • Dumperth C (2019) Three-dimensional numerical investigations on Huangtupo landslide (PR China): from the slope reconstruction to the implementation into an integrative early warning system

  • Ghaderi A, Abbaszadeh SA, Larsson S (2022) A visualized hybrid intelligent model to delineate Swedish fine-grained soil layers using clay sensitivity. An Interdisciplinary J Soil Sci Hydrology Geomorphology Focusing Geoecol Landscape Evol Catena, 214

    Google Scholar 

  • Gharagoz MM, Noureldin M, Kim J (2023) Machine learning-based design of a seismic retrofit frame with spring-rotational friction dampers. Eng Struct 292

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112(1–2):42–66

    Article  Google Scholar 

  • Hong H, Tsangaratos P, Ilia I, Loupasakis C, Wang Y (2020) Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping. Sci Total Environ 742:140549

    Article  CAS  Google Scholar 

  • Huang F, Cao Z, Guo J, Jiang SH, Guo Z (2020) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580

    Article  Google Scholar 

  • Janizadeh S et al (2023) Potential impacts of future climate on the spatio-temporal variability of landslide susceptibility in Iran using machine learning algorithms and CMIP6 climate-change scenarios. Gondwana Res 124:1–17

    Article  Google Scholar 

  • Kelman I (2020) Oxford university press: disaster by choice: how our actions turn natural hazards into catastrophes. Engineering Technol E&T IET Eng Technol 2: 15

  • Li X, Fu J, Jiang D, Lin G, Cao C (2022) Land use optimization in Ningbo City with a coupled GA and PLUS model. J Clean Prod 375

  • Li M, Wang H, Chen J, Zheng K (2024) Assessing landslide susceptibility based on the random forest model and multi-source heterogeneous data. Ecol Ind 158

  • Liang M (2023) Environmental Monitoring and Prevention of Geological Hazards Based on Multi-Source Remote Sensing Technology: Taking the Southeast Tibet Region as an Example. Proceedings Article

  • Lu H et al (2024) Active landslide detection using integrated remote sensing technologies for a wide region and multiple stages: a case study in southwestern China. Sci Total Environ, 172709

  • Ma X, Rong H, Yan Y (2024) GPS-RTK mapping technology in remote sensing dynamic monitoring of geological disasters under artificial intelligence technology. In: Second international conference on data science and information system (ICDSIS). IEEE

  • Moghimi A, Singha C, Fathi M, Pirasteh S, Mohammadzadeh A, Varshosaz M et al (2024) Hybridizing genetic random forest and self-attention based Cnn-Lstm algorithms for landslide susceptibility mapping in darjiling and kurseong, India. Quaternary Sci Adv, 14

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the inje area, Korea. Environ Earth Ences 68(5):1443–1464

    Article  Google Scholar 

  • Pham BT, Prakash, Bui, DT (2018) Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology 2018(303–):256–270

  • Qiu C et al (2022) A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate. Sci Total Environ, 818

  • Rai SC et al (2024) Landslide susceptibility analysis in the Bhilangana Basin (India) using GIS-based machine learning methods. Geosyst Geoenvironment 3(2)

  • Saha A et al (2022) Development and Assessment of GIS-based landslide susceptibility mapping models using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India. Land 11(10)

  • Saha A, Villuri VG, Bhardwaj A (2023) Development and assessment of a novel hybrid machine learning-based landslide susceptibility mapping model in the Darjeeling Himalayas. Stochastic Env Res Risk Assessment

  • Saha A, Tripathi L, Villuri VGK, Bhardwaj A (2024) Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology. Environ Sci Pollut Res Int 31(7):10443–10459

    Article  Google Scholar 

  • Song K, Yan F, Ding T, Gao L, Lu S (2020) A steel property optimization model based on the XGBoost algorithm and improved PSO. Comput Mater Sci 174:109472

    Article  CAS  Google Scholar 

  • Sun HWD (2021) Assessment of landslide susceptibility mapping based on bayesian hyperparameter optimization: a comparison between logistic regression and random forest. Eng Geol 281(1)

  • Sun DL, Jing Wang H, Wen YK, Ding, Changlin Mi (2024) Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: a case of Wanzhou District, China. J Rock Mechanics Geotechnical Eng

  • Tang J (2023) Fractional gradient descent algorithm for switching models using self-organizing maps: one set data or all the collected data. Chaos Solitons Fractals Appl Sci Eng Interdisciplinary J Nonlinear Sci 172

  • Tang X, Shi C, Zhang Y (2024) Inversion Study on Landslide Seepage Field based on Swarm Intelligence Optimization least-square support Vector Machine Algorithm. Appl Sci 14(13):5822

    Article  CAS  Google Scholar 

  • Thiebes B (2012) Landslide analysis and early warning systems: local and regional case study in the swabian alb, Germany. Springer Sci. & Bus. Med

  • Wallach D, Martre P, Liu B et al (2018) Multimodel ensembles improve predictions of crop environment–management interactions. Glob Chan Bio 24(11):5072–5083

    Article  Google Scholar 

  • Wang T, Bian Y, Zhang Y, Hou X (2022) Classification of earthquakes, explosions and mining-induced earthquakes based on xgboost algorithm. Comput Geosci 170:105242

    Article  Google Scholar 

  • Wei C, Mahdi, Panahi H, Reza et al (2017) Performance evaluation of gis-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (anfis) with genetic algorithm (Ga), differential evolution (de), and particle swarm optimization (pso) for landslide spatial modelling. CATENA 157:310–324

    Article  Google Scholar 

  • Xie C, Wang S, Yu Y, Deng Y (2024) An Assessment of Geological Hazard Management using dynamically stabilized recurrent neural network and Beluga Whale optimization Algorithm. J Electrical Syst

  • Xu K, Zhao Z, Chen W, Ma J, Liu F, Zhang Y, Ren Z (2024) Comparative study on landslide susceptibility mapping based on different ratios of training samples and testing samples by using RF and FR-RF models. Nat Hazards Res 4(1):62–74

    Article  Google Scholar 

  • Ye Z, Wang JG, Yang J (2024) A multi-objective optimization approach for a fault geothermal system based on response surface method. Geothermics

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856

    Article  Google Scholar 

  • Zhang J, Ma X, Zhang J, Sun D, Zhou X, Mi C, Wen H (2023a) Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. J Environ Manage. 2023; 332:117357

  • Zhang W et al (2023b) Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms. Gondwana Res 123:41–53

    Article  Google Scholar 

  • Zhao Z, Liu ZY, Xu C (2021) Slope unit-based landslide susceptibility mapping using certainty factor, support vector machine, random forest, CF-svm and CF-RF models. Front Earth Sci

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Funding

This paper is supported by Science and Technology Project of Hebei Education Department (BJK2022010), the Science and Technology Research and Development Plan of Shijiazhuang under Grant (241791097 A).

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Authors and Affiliations

  1. School of Civil Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050018, P.R. China

    Junjie Jiang, Qizhi Wang & Shihao Luan

  2. Innovation Center of Disaster Prevention and Mitigation Technology for Geotechnical and Structural Systems of Hebei Province, Shijiazhuang, Hebei, 050018, P.R. China

    Junjie Jiang, Qizhi Wang & Shihao Luan

  3. Hebei Key Laboratory of Geological Resources and Environment Monitoring and Protection, 58 Xingyuan Street, Shijiazhuang, Hebei, 050021, P.R. China

    Minghui Gao, Huijie Liang & Jun Zheng

  4. Hebei Geo-Environment Monitoring, 58 Xingyuan Street, Shijiazhuang, Hebei, 050021, P.R. China

    Minghui Gao, Huijie Liang & Jun Zheng

  5. Shijiazhuang Tiedao University, Shijiazhuang, Hebei, 050043, P.R. China

    Wei Yuan & Xiaolei Ji

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  1. Junjie Jiang
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Contributions

Junjie Jiang: Responsible for data computation, preparation of charts and tables.Qizhi Wang: Supervised the entire research process and provided research guidance.Shihao Luan: Responsible for writing the paper.Minghui Gao, Huijie Liang, and Jun Zheng: Provided research resources.Wei Yuan and Xiaolei Ji: Participated in data analysis.All authors reviewed the manuscript. The authors declare no competing interests.

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Correspondence to Qizhi Wang.

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Communicated by Hassan Babaie.

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Jiang, J., Wang, Q., Luan, S. et al. Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm. Earth Sci Inform 17, 5539–5559 (2024). https://doi.org/10.1007/s12145-024-01470-9

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