Abstract
Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps. Therefore, this research aims to compare the accuracy of the novel evolutionary methods of landslide susceptibility mapping. To achieve this, a unique method that integrates two techniques from Machine Learning and Neural Networks with novel geomorphological indices is used to calculate the landslide susceptibility index (LSI). The study was conducted in western Azerbaijan, Iran, where landslides are frequent. Sixteen geology, environment, and geomorphology factors were evaluated, and 160 landslide events were analyzed, with a 30:70 ratio of testing to training data. Four Support Vector Machine (SVM) algorithms and Artificial Neural Network (ANN)-MLP were tested. The study outcomes reveal that utilizing the algorithms mentioned above results in over 80% of the study area being highly sensitive to large-scale movement events. Our analysis shows that the geological parameters, slope, elevation, and rainfall all play a significant role in the occurrence of landslides in this study area. These factors obtained 100%, 75.7%, 68%, and 66.3%, respectively. The predictive performance accuracy of the models, including SVM, ANN, and ROC algorithms, was evaluated using the test and train data. The AUC for ANN and each machine learning algorithm (Simple, Kernel, Kernel Gaussian, and Kernel Sigmoid) was 0.87% and 1, respectively. The Classification Matrix algorithm and Sensitivity, Accuracy, and Specificity variables were used to assess the models’ efficacy for prediction purposes. Results indicate that machine learning algorithms are more effective than other methods for evaluating areas’ sensitivity to landslide hazards. The Simple SVM and Kernel Sigmoid algorithms performed well, with a performance score of one, indicating high accuracy in predicting landslide-prone areas.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Adnan Ikram RM, Khan I Moayedi, H, et al (2023) Novel evolutionary-optimized neural network for predicting landslide susceptibility. Environ Dev Sustain. https://doi.org/10.1007/s10668-023-03356-0
Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102–109. https://doi.org/10.1016/j.rser.2014.01.069
Alimohammadlou Y, Najafi A, Gokceoglu C (2014) Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province Iran. CATENA 120:149–162. https://doi.org/10.1016/j.catena.2014.04.009
Alqadhi S, Mallick J, Talukdar S, Bindajam AA, Saha TK, Ahmed M, Khan RA (2022) Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping. Geocarto Int 37(25):9518–9543. https://doi.org/10.1080/10106049.2021.2022009
Arnone E, Francipane A, Scarbaci A, Puglisi C, Noto LV (2016) Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping. Environ Model Software 84:467–481. https://doi.org/10.1016/j.envsoft.2016.07.016
Aslam B, Zafar A, Khalil U (2023) Comparative analysis of multiple conventional neural networks for landslide susceptibility mapping. Nat Hazards 115(1):673–707. https://doi.org/10.1007/s11069-022-05570-x
Asmare D (2023) Application and validation of AHP and FR methods for landslide susceptibility mapping around choke mountain, northwestern Ethiopia. Sci African 19:e01470. https://doi.org/10.1016/j.sciaf.2022.e01470
Bravo-López E, Fernández Del Castillo T, Sellers C, Delgado-García J (2022) Landslide susceptibility mapping of landslides with Artificial Neural Networks: multi-approach analysis of backpropagation algorithm applying the Neuralnet Package in Cuenca Ecuador. Remote Sens 14(14):3495
Chen J, Wen L, Bi C, Liu Z, Liu X, Yin L, Zheng W (2023) Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt. Open Geosciences, 15(1). https://doi.org/10.1515/geo-2022-0482
Chen W, Chen X, Peng J, Panahi M, Lee S (2021) Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geosci Front 12(1):93–107. https://doi.org/10.1016/j.gsf.2020.07.012
Chen W, Li Y (2020) GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models.". CATENA 195:104777. https://doi.org/10.1016/j.catena.2020.104777
Chen Y, Chen Z, Guo D, Zhao Z, Lin T, Zhang C (2022) Underground space use of urban built-up areas in the central city of Nanjing: insight based on a dynamic population distribution. Underground Space 7(5):748–766. https://doi.org/10.1016/j.undsp.2021.12.006
Dang P, Cui J, Liu Q, Li Y (2023) Influence of source uncertainty on stochastic ground motion simulation: a case study of the 2022 Mw 6.6 Luding, China, earthquake. Stochastic Environ Res Risk Assess 37(8):2943–2960. https://doi.org/10.1007/s00477-023-02427-y
Dao DV, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Phong TV, Ly H-B, Le T-T, Trinh PT, Luu C, Quoc NK, Thanh BN, Pham BT (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA 188:104451. https://doi.org/10.1016/j.catena.2019.104451
Daviran M, Shamekhi M, Ghezelbash R, Maghsoudi A (2023) Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm. Int J Environ Sci Technol 20(1):259–276. https://doi.org/10.1007/s13762-022-04491-3
Dong J, Niu R, Li B, Xu H, Wang S (2023) Potential landslides identification based on temporal and spatial filtering of SBAS-InSAR results. Geomatics Nat Hazards Risk 14(1):52–75. https://doi.org/10.1080/19475705.2022.2154574
Dou J, Yunus AP, Merghadi A, Shirzadi A, Nguyen H, Hussain Y, Avtar R, Chen Y, Pham BT, Yamagishi H (2020) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ 720:137320. https://doi.org/10.1016/j.scitotenv.2020.137320
Gao C, Zhang B, Shao S, Hao M, Zhang Y, Xu Y, Kuang Y, Dong L, Wang Z (2023) Risk assessment and zoning of flood disaster in Wuchengxiyu Region. China. Urban Clim 49:101562. https://doi.org/10.1016/j.uclim.2023.101562
Huang F, Cao Z, Guo J, Jiang S-H, Li S, Guo Z (2020) "Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580. https://doi.org/10.1016/j.catena.2020.104580
Huang F, Yan J, Fan X, Yao C, Huang J, Chen W, Hong H (2022) Uncertainty pattern in landslide susceptibility prediction modelling: effects of different landslide boundaries and spatial shape expressions. Geosci Front 13(2):101317. https://doi.org/10.1016/j.gsf.2021.101317
Huang S, Huang M, Lyu Y (2021) Seismic performance analysis of a wind turbine with a monopile foundation affected by sea ice based on a simple numerical method. Eng Appl Comput Fluid Mech 15(1):1113–1133. https://doi.org/10.1080/19942060.2021.1939790
Huang S, Lyu Y, Sha H, Xiu L (2021) Seismic performance assessment of unsaturated soil slope in different groundwater levels. Landslides 18(8):2813–2833. https://doi.org/10.1007/s10346-021-01674-w
Huang W, Ding M, Li Z, Yu J, Ge D, Liu Q, Yang J (2023) Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms. CATENA 222:106866. https://doi.org/10.1016/j.catena.2022.106866
Hung C, Chen J-H (2009) A selective ensemble based on expected probabilities for bankruptcy prediction. Expert Syst Appl 36(3 Part 1):5297–5303. https://doi.org/10.1016/j.eswa.2008.06.068
Ikram RMA, Dehrashid AA, Zhang B et al (2023a) A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment. Stoch Environ Res Risk Assess 37:1717–1743. https://doi.org/10.1007/s00477-022-02361-5
Ikram RMA, Dehrashid AA, Zhang B, Chen Z, Le BN, Moayedi H (2023b) A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment. Stochastic Environ Res Risk Assess 37(5):1717–1743. https://doi.org/10.1007/s00477-022-02361-5
Jia S, Dai Z, Zhou Z, Ling H, Yang Z, Qi L, Wang Z, Zhang X, Thanh HV, Soltanian MR (2023) Upscaling dispersivity for conservative solute transport in naturally fractured media. Water Res 235:119844. https://doi.org/10.1016/j.watres.2023.119844
Jiang C, Kang Y, Qu K, Long Y, Ma Y, Yan S (2023) Towards a high-resolution modelling scheme for local-scale urban flood risk assessment based on digital aerial photogrammetry. Eng Appl Comput Fluid Mech 17(1):2240392. https://doi.org/10.1080/19942060.2023.2240392
Khan M, RazaNaqvi S, Ullah Z, Ali AmmarTaqvi S, NoumanAslam Khan M, Farooq W, Taqimehran M, Juchelková D, Štěpanec L (2023) Applications of machine learning in thermochemical conversion of biomass-a review. Fuel 332:126055. https://doi.org/10.1016/j.fuel.2022.126055
Li W, Zhu J, Fu L, Zhu Q, Xie, Y.,... Hu, Y, (2021) An augmented representation method of debris flow scenes to improve public perception. International Journal of Geographical Information Science 35(8):1521–1544. https://doi.org/10.1080/13658816.2020.1833016
Li Q, Song D, Yuan C, Nie W (2022) An image recognition method for the deformation area of open-pit rock slopes under variable rainfall. Measurement 188. https://doi.org/10.1016/j.measurement.2021.110544
Liao M, Wen H, Yang L (2022) Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: a case of Wushan and Wuxi counties, China. CATENA 217:106428. https://doi.org/10.1016/j.catena.2022.106428
Liu H, Ding F, Li J, Meng X, Liu C, Fang H (2023) Improved detection of buried elongated targets by dual-polarization GPR. IEEE Geosci Remote Sens Lett 20:1–5. https://doi.org/10.1109/LGRS.2023.3243908
Liu S, Wang L, Zhang W, He Y, Pijush S (2023) A comprehensive review of machine learning-based methods in landslide susceptibility mapping. Geol J 58(6):2283–2301
Ma S, Qiu H, Yang D, Wang J, Zhu Y, Tang, B.,... Cao, M, (2023) Surface multi-hazard effect of underground coal mining. Landslides 20(1):39–52. https://doi.org/10.1007/s10346-022-01961-0
Martinello C, Cappadonia C, Rotigliano E (2023) Investigating the effects of cell size in statistical landslide susceptibility modelling for different landslide typologies: a test in Central-Northern Sicily. Appl Sci 13(2):1145
Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225
Moayedi H, Canatalay PJ, AhmadiDehrashid A, Cifci MA, Salari M, Le BN (2023a) Multilayer perceptron and their comparison with two nature-inspired hybrid techniques of biogeography-based optimization (BBO) and backtracking search algorithm (BSA) for assessment of landslide susceptibility. Land 12(1):242
Moayedi H, Dehrashid AA (2023b) A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping. Environ Sci Pollut Res 30(34):82964–82989. https://doi.org/10.1007/s11356-023-28133-4
Moayedi H, Salari M, Dehrashid AA et al (2023c) Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain. Stoch Environ Res Risk Assess 37:2961–2976. https://doi.org/10.1007/s00477-023-02429-w
Moayedi H, Le Van B (2022) The applicability of Biogeography-Based Optimization and Earthworm Optimization Algorithm hybridized with ANFIS as reliable solutions in estimation of cooling load in buildings. Energies 15(19):7323
Moayedi H, Osouli A, Bui DT, KokFoong L, Nguyen H, Kalantar B (2019) Two novel neural-evolutionary predictive techniques of dragonfly algorithm (DA) and Biogeography-Based Optimization (BBO) for landslide susceptibility analysis. Geomatics, Nat Hazards Risk 10(1):2429–2453. https://doi.org/10.1080/19475705.2019.1699608
Mondal S, Maiti R (2013) Integrating the Analytical Hierarchy Process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya. Int J Dis Risk Sci 4(4):200–212. https://doi.org/10.1007/s13753-013-0021-y
Nguyen H, Mehrabi M, Kalantar B, Moayedi H, Abdullahi MAM (2019) Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping. Geomatics Nat Hazards Risk 10(1):1667–1693
Nwazelibe VE, Unigwe CO, Egbueri JC (2023) Testing the performances of different fuzzy overlay methods in GIS-based landslide susceptibility mapping of Udi Province, SE Nigeria. CATENA 220:106654. https://doi.org/10.1016/j.catena.2022.106654
Osna T, Sezer EA, Akgun A (2014) GeoFIS: An integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30. https://doi.org/10.1016/j.cageo.2013.12.016
Pei J, Gong J, Wang Z (2020) Risk prediction of household mite infestation based on machine learning. Build Environ 183:107154. https://doi.org/10.1016/j.buildenv.2020.107154
Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. CATENA 186:104249. https://doi.org/10.1016/j.catena.2019.104249
Sun D, Shi S, Wen H, Xu J, Zhou X, Wu J (2022) A hybrid optimization method of factor screening predicated on geodetector and random forest for landslide susceptibility mapping." Geomorphology 379. https://doi.org/10.1016/j.geomorph.2021.107623
Tian H, Huang N, Niu Z, Qin Y, Pei J, Wang J (2019) Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Remote Sens 11(7):820
Tian H, Pei J, Huang J, Li X, Wang J, Zhou, B.,... Wang, L, (2020) Garlic and winter wheat identification based on active and passive satellite imagery and the google earth engine in northern china. Remote sensing (Basel, Switzerland) 12(21):3539. https://doi.org/10.3390/rs12213539
Wang W, Li D-Q, Tang X-S, Du W (2023) Seismic fragility and demand hazard analyses for earth slopes incorporating soil property variability. Soil Dynam Earthquake Eng 173:108088. https://doi.org/10.1016/j.soildyn.2023.108088
Wang X, Wang T, Xu J, Shen Z, Yang Y, Chen A, Wang S, Liang E, Piao S (2022) Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion. Nat Ecol Evol 6(7):890–899. https://doi.org/10.1038/s41559-022-01774-3
Wang Y, Fang Z, Wang M, Peng L, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput Geosci 138:104445. https://doi.org/10.1016/j.cageo.2020.104445
Wang W, Chen Z, Yuan X (2022) Simple low-light image enhancement based on Weber-Fechner law in logarithmic space. Signal processing. Image communication 106. https://doi.org/10.1016/j.image.2022.116742
Wu B, Quan Q, Yang S, Dong Y (2023) A social-ecological coupling model for evaluating the human-water relationship in basins within the Budyko framework. Journal of Hydrology 619. https://doi.org/10.1016/j.jhydrol.2023.129361
Xie X, Tian Y, Wei G (2023) Deduction of sudden rainstorm scenarios: integrating decision makers’ emotions, dynamic Bayesian network and DS evidence theory. Nat Hazards 116(3):2935–2955. https://doi.org/10.1007/s11069-022-05792-z
Xu J, Zhou G, Su S, Cao Q, Tian Z (2022) The development of a rigorous model for bathymetric mapping from multispectral satellite-images. Remote Sens 14(10):2495
Yang D, Qiu H, Ye B, Liu Y, Zhang J, Zhu Y (2023) Distribution and recurrence of warming-induced retrogressive thaw slumps on the Central Qinghai-Tibet Plateau. J Geophys Res: Earth Surf e2022JF007047. doi. https://doi.org/10.1029/2022JF007047
Yin L, Wang L, Ge L, Tian J, Yin Z, Liu M, Zheng W (2023) Study on the thermospheric density distribution pattern during geomagnetic activity. Appl Sci 13(9):5564
Yin L, Wang L, Zheng W, Ge L, Tian J, Liu, Y.,... Liu, S, (2022) Evaluation of empirical atmospheric models using swarm-c satellite data. Atmosphere 13(2):294. https://doi.org/10.3390/atmos13020294
Yu C, Chen J (2020) Landslide susceptibility mapping using the slope unit for Southeastern Helong City, Jilin Province, China: a comparison of ANN and SVM. Symmetry 12(6):1047
Yuan C, Moayedi H (2020) Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence. Eng Comput 36(4):1801–1811. https://doi.org/10.1007/s00366-019-00798-x
Yue Z, Zhou W, Li T (2021) Impact of the Indian Ocean dipole on evolution of the subsequent ENSO: relative roles of dynamic and thermodynamic processes. J Clim 34(9):3591–3607
Zeng T, Wu L, Peduto D, Glade T, Hayakawa YS, Yin K (2023) Ensemble learning framework for landslide susceptibility mapping: different basic classifier and ensemble strategy. Geosci Front 14(6):101645. https://doi.org/10.1016/j.gsf.2023.101645
Zhao Y, Wang R, Jiang Y, Liu H, Wei Z (2019) GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China. Eng Geol 259:105147. https://doi.org/10.1016/j.enggeo.2019.105147
Zhang C, Yin Y, Yan H, Zhu S, Li B, Hou, X.,... Yang, Y, (2022) Centrifuge modeling of multi-row stabilizing piles reinforced reservoir landslide with different row spacings. Landslides. https://doi.org/10.1007/s10346-022-01994-5
Zhou C, Yin K, Cao Y, Ahmed B (2016) Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng Geol 204:108–120. https://doi.org/10.1016/j.enggeo.2016.02.009
Zhou G, Deng R, Zhou X, Long S, Li W, Lin G, Li X (2022a) Gaussian inflection point selection for LiDAR hidden echo signal decomposition. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2021.3107438
Zhuo Z, Du L, Lu X, Chen J, Cao Z (2022b) Smoothed Lv distribution based three-dimensional imaging for spinning space debris. IEEE Trans Geosci Remote Sens 60:1–13. https://doi.org/10.1109/TGRS.2022.3174677
Zhou G, Li W, Zhou X, Tan Y, Lin G, Li X, Deng R (2021a) An innovative echo detection system with STM32 gated and PMT adjustable gain for airborne LiDAR. International Journal of Remote Sensing 42(24):9187–9211. https://doi.org/10.1080/01431161.2021.1975844
Zhou G, Zhou X, Song Y, Xie D, Wang L, Yan, G.,... Wang, H, (2021b) Design of supercontinuum laser hyperspectral light detection and ranging (LiDAR) (SCLaHS LiDAR). International journal of remote sensing 42(10):3731–3755. https://doi.org/10.1080/01431161.2021.1880662
Acknowledgements
We want to express our appreciation to all the participants, without whom this study would be impossible.
Author information
Authors and Affiliations
Contributions
Conceptualization, methodology, formal analysis, and supervision were performed by Y.SH and A. A-D. Investigation and result interpretation were performed by R. A-B and H. M. Writing—original draft preparation was performed by B. N.
Corresponding authors
Ethics declarations
Ethical approval
All ethical responsibilities regarding the publication of this paper are considered.
Consent to participate
All authors have participated in the final version of the manuscript.
Consent to publish
All authors have read and agreed to the published version of the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Additional information
Responsible Editor: Philippe Garrigues
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shen, Y., Ahmadi Dehrashid, A., Bahar, R.A. et al. A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran. Environ Sci Pollut Res 30, 123527–123555 (2023). https://doi.org/10.1007/s11356-023-30762-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-30762-8