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Landslide susceptibility map** using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA

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Abstract

To be proactive in mountain hazard mitigation, landslide disaster assessments are becoming increasingly urgent. In this study, three modeling techniques, namely, support vector machine (SVM), convolutional neural network (CNN-1D), and (CNN-2D), were applied and their outcomes were compared for landslide susceptibility map** at Asir Region, Saudi Arabia. As a first step, a landslide inventory map was developed from various data sources. A total of 181 landslide points were identified and divided into 70% training and 30% validation datasets. Thirteen landslide indicator factors (LIFs) were used, including elevation, aspect, distance to fault, geology, land use, plan and profile curvature, distance to road, slope length (LS), stream power index (SPI), topographic witness index (TWI), slope angle, and distance to streams. Experimental results of model accuracy using receiver operating characteristics and area under the curve (ROC, AUC), mean absolute error (MAE), and kappa index (K) showed that the CNN-1D and CNN-2D models (ROC = 86% and 89%, respectively) were more accurate than conventional machine learning model (SVM) (ROC = 82%) in predicting landslides spatially. Specifically, the results showed that CNN-1D and CNN-2D were 4.9% and 7.9% better than support vector machine (SVM) in terms of ROC, and that CNN-2D was 3.5% better than CNN-1D. Moreover, other statistical indices showed that CNN-2D produce the highest value of kappa index (0.855) and lowest value of mean absolute error (0.072), whereas SVM provides the lowest value of kappa index (0.562) and highest value of mean absolute error (0.223). Results indicate that the CNN-2D model is the optimal model for landslide susceptibility map**. The generated hazard maps are a crucial step in landslide prevention and management to identify the future landslides and avoid potentially problematic areas.

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References

  • Abu Abdullah MM, Youssef AM, Maerz NH, Abu-AlFadail E, Al-Harbi HM, Al-Saadi NS (2020) A flood risk management program of Wadi Baysh dam on the downstream area: an integration of hydrologic and hydraulic models, Jizan Region. KSA Sustainability 12:1069. https://doi.org/10.3390/su12031069

    Article  Google Scholar 

  • Abujayyab SKM, Saleh A (2020) Landslides risk prediction using cascade neural networks model at Muş in Turkey. IOP Conf Sr Earth Environ Sci 540:012081

  • Alvioli M, Baum RL (2016) Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface. Environ Model Softw 81:122–135. https://doi.org/10.1016/j.envsoft.2016.04.002

    Article  Google Scholar 

  • Andrieu C, De Freitas N, Doucet A, Jordan MI (2003) An introduction to MCMC for machine learning. Mach Learn 50:5–43

    Article  Google Scholar 

  • Arabameri A, Saha S, Roy J, Chen W, Blaschke T, Bui DT (2020) Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River watershed. Iran Remote Sensing 12(3):475. https://doi.org/10.3390/rs12030475

    Article  Google Scholar 

  • Awad M, Khanna R (2015) Support vector machines for classification. In: Efficient learning machines. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_3

  • Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility map**. Sci Rep 11:24112. https://doi.org/10.1038/s41598-021-03585-1

    Article  Google Scholar 

  • Bahrami S, Rahimzadeh B, Khaleghi S (2020) Analyzing the effects of tectonic and lithology on the occurrence of landslide along Zagros ophiolitic suture: a case study of Sarv-Abad, Kurdistan. Iran Bulletin of Engineering Geology and the Environment 79:1619–1637. https://doi.org/10.1007/s10064-019-01639-3

    Article  Google Scholar 

  • Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798–1828

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrological Sci J 24(1):43–69

    Article  Google Scholar 

  • Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159. https://doi.org/10.1016/s0031-3203(96)00142-2

    Article  Google Scholar 

  • Brenning A, Schwinn M, Ruiz-Páez AP (2015) Muenchow J (2015) Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province. Nat Hazards Earth Syst Sci 15:45–57. https://doi.org/10.5194/nhess-15-45-2015

    Article  Google Scholar 

  • Carrio A, Sampedro C, Rodriguez-Ramos A, Campoy P (2017) A review of deep learning methods and applications for unmanned aerial vehicles, Journal of Sensors, 2017. Article ID 3296874:13. https://doi.org/10.1155/2017/3296874

    Article  Google Scholar 

  • Champati Ray PK, Lakhera RC (2004) Landslide Hazards in India, Proc. Asian Workshop on Regional Capacity Enhancement for Landslide Mitigation (RECLAIM), organized by Asian Disaster Preparedness Centre (ADPC), Bangkok and Norwegian Geo-technical Institute, Oslo, Bangkok, 13–15 Sep. 2004.

  • Chatterjee S, Simonoff JS (2013) Handbook of regression analysis. Wiley, New York, NY

    Google Scholar 

  • Chen C-Y (2009) Sedimentary impacts from landslides in the Tachia River basin. Taiwan Geomorphology 105:355–365. https://doi.org/10.1016/j.geomorph.2008.10.009

    Article  Google Scholar 

  • Chen W, Shahabi H, Shirzadi A, Li T, Guo C, Hong H, Li W, Pan D, Hui J, Ma M (2018) A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int 1–23

  • Chen W, Hong H, Panahi M, Shahabi H, Wang Y, Shirzadi A, Pirasteh S, Alesheikh AA, Khosravi K, Panahi S, Rezaie F (2019) Spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo) Appl. Sci 9(18):3755

    Google Scholar 

  • Christianini N, Shawe-Taylor J (2000) An introduction to support vector machines; Cambridge University Press; ISBN 0521780195.

  • Colkesen I, Sahin EK, Kavzoglu T (2016) Susceptibility map** of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sci 118:53–64

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-Vector Networks Mach Learn 20(3):273–297

    Google Scholar 

  • Dagdelenler G, Nefeslioglu HA, Gokceoglu C (2016) Modification of seed cell sampling strategy for landslide susceptibility map**: an application from the eastern part of the Gallipoli peninsula (Canakkale, Turkey). Bull Eng Geol Environ 75(2):575–590

    Article  Google Scholar 

  • Dai F, Lee C, Li J, Xu Z (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391

    Article  Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital RR, Althuwaynee OF (2013) Landslide susceptibility map** using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards 65:135–165. https://doi.org/10.1007/s11069-012-0347-6

    Article  Google Scholar 

  • Ding A, Zhang Q, Zhou X, Dai B (2016) Automatic recognition of landslide based on CNN and texture change detection Proceedings of the Chinese Association of Automation (YAC), Youth Academic Annual Conference, Wuhan, China, 11–13 November 2016, IEEE, 444–448. https://doi.org/10.1109/YAC.2016.7804935

  • Ding Q, Chen W, Hong H (2017) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility map**. Geocarto Int 32(6):619–639

  • Donnarumma A, Revellino P, Grelle G, Guadagno FM (2013) Slope angle as indicator parameter of landslide susceptibility in a geologically complex area. In: Margottini C, Canuti P, Sassa K. (eds) Landslide Science and Practice. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31325-7_56

  • Elfeki AM, Ewea HA, Al-Amri NS (2014) Development of storm hyetographs for flood forecasting in the Kingdom of Saudi Arabia. Arab J Geosci 7:4387–4398. https://doi.org/10.1007/s12517-013-1102-3

    Article  Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2020) Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility map**. Comput Geosci 139:104470

  • Fang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility map**. Int J Geogr Inf Sci 35(2):321–347. https://doi.org/10.1080/13658816.2020.1808897

    Article  Google Scholar 

  • Fairer GM (1985) Geologic map of the wadi Baysh quadrangle, sheet 17F, Kingdom of Saudi Arabia: Saudi Arabian Deputy Ministry for Mineral Resources Geoscience map GM-77 A, C, Scale 1:250000.

  • Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Rem Sens 11(2):196

    Article  Google Scholar 

  • Greenwood WR (1985) Geologic Map of the Abha Quadrangle, Sheet 18 F, Kingdom of Saudi Arabia Ministry of Petroleum and Mineral Resources. Deputy Ministry for Mineral Resources GM-75 c, scale 1:250000

  • Greenwood WR, Anderson RE, Flcek RJ, Roberts RJ (1981) Precambrian geologic history and plate tectonic evaluation of the Arabian Shield. Saudi Arabia. DGMR, Bull 24:1–35

    Google Scholar 

  • Greenwood WR, Stoeser DB, Fleck RJ, Stacey JS (1982) late Proterozoic island-arc complexes and tectonic belts in the southern part of the Arabian Sheild, Kingdom of Saudi Arabia; Saudi Arabian Deputy Ministry for Mineral Resources Open File Report USGS-OF-02–8 46p

  • Guha-Sapir D, Below R, Hoyois P (2020) EM-DAT: international disaster database. Brussels, Belgium: Université Catholique de Louvain. Available from: http://www.emdat.be

  • Guillard C, Zezere J (2012) Landslide Susceptibility Assessment and Validation in the Framework of Municipal Planning in Portugal: The Case of Loures Municipality. Environmental Management 50, 721–735. https://doi.org/10.1007/s00267-012-9921-7

    Article  Google Scholar 

  • Guo C, David RM, Zhang Y, Wang K, Yang Z (2015) Quantitative assessment of landslide susceptibility along the **anshuihe fault zone, Tibetan plateau, China. Geomorphology 248:93–110Return to ref 2015 in article

  • Guzzetti F, Cardinali M, Reichenbach P, Carrara A (2000) Comparing landslide maps: a case study in the upper Tiber River Basin Central Italy. Environ Manag 25(3):247–363

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probablistic Landslide Hazard Assessment at the Basin Scale. Geophys J Roy Astron Soc 72. https://doi.org/10.1016/j.geomorph.2005.06.002

  • Guzzetti F, Ardizzone F, Cardinali M, Galli M, Reichenbach P, Rossi M (2008) Distribution of landslides in the upper Tiber River basin, Central Italy. Geomorphology 96:105–122. https://doi.org/10.1016/j.geomorph.2007.07.015

    Article  Google Scholar 

  • 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 112:42–66. https://doi.org/10.1016/j.earscirev.2012.02.001

  • Hasanean H, Almazroui M (2015) Rainfall: features and variations over Saudi Arabia. A Review Climate 3(3):578–626. https://doi.org/10.3390/cli3030578

    Article  Google Scholar 

  • Huang F, Chen J, Du Z, Yao C, Huang J, Jiang Q, Chang Z, Li S (2020) Landslide susceptibility prediction considering regional soil erosion based on machine-learning models. ISPRS Int J Geo Inf 9(6):377. https://doi.org/10.3390/ijgi9060377

    Article  Google Scholar 

  • Huang F, Zhang J, Zhou C, Wang Y, Huang J, Zhu L (2019) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17:217–229

    Article  Google Scholar 

  • Jaafari A, Zenner EK (2018) Pham BT (2018) Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers. Ecol Inform 43:200–211

    Article  Google Scholar 

  • James G, Witten D, Hastie T, Tibshirani R (2013) An Introduction to Statistical Learning Springer New York

  • Jenks GF, Caspall FC (1971) Error on choroplethic maps: definition, measurement, reduction. Ann Assoc Am Geogr 61(2)(1971):217–244

  • Karantanellis E, Marinos V, Vassilakis E, Hölbling D (2021) Evaluation of Machine Learning Algorithms for Object-Based Map** of Landslide Zones Using UAV Data. Geosciences 11:305. https://doi.org/10.3390/geosciences11080305

    Article  Google Scholar 

  • Keyport RN, Oommen T, Martha TR, Sa**kumar KS, Gierke JS (2018) A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images Int. J Appl Earth Obs Geoinf 64:1–11. https://doi.org/10.1016/j.jag.2017.08.015

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 1097–1105

  • Kumar D, Thakur M, Dubey CS, Shukla DP (2017) Landslide susceptibility map** & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology 295:115–125. https://doi.org/10.1016/j.geomorph.2017.06.013

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436

    Article  Google Scholar 

  • Lee S, Kim J-C, Jung H-S, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city. Korea Geomat Nat Hazards Risk 8(2):1185–1203. https://doi.org/10.1080/19475705.2017.1308971

    Article  Google Scholar 

  • Li J, Wang W, Han Z (2021a) A variable weight combination model for prediction on landslide displacement using AR model, LSTM model, and SVM model: a case study of the **nming landslide in China. Environ Earth Sci 80(10):386

    Article  Google Scholar 

  • Li J, Wang W, Han Z, Chen G (2021b) Analysis of secondary-factor combinations of landslides using improved association rule algorithms: a case study of Kitakyushu in Japan. Geomat Nat Haz Risk 12(1):1885–1904. https://doi.org/10.1080/19475705.2021.1947904

    Article  Google Scholar 

  • Li XJ, Cheng XW, Chen WT, Chen G, Liu SW (2015) Identification of forested landslides using Lidar data, object-based image analysis, and machine learning algorithms Rem. Sens 7(8):9705–9726

    Google Scholar 

  • Ma Z, Mei G, Piccialli F (2021) Machine learning for landslides prevention: a survey. Neural Comput & Applic 33:10881–10907

    Article  Google Scholar 

  • Mandal K, Saha S, Mandal S (2021) Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India. Geosci Front 12(5) 101203

  • McClure HA (1980) Permian-Carboniferous glaciation in the Arabian Peninsula. Geol Soc Am Bull 91(1):707–712

    Article  Google Scholar 

  • Merghadi A, Yunus AP, Dou J, Whiteley J, Pham BT, 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

  • Miller S, Brewer T, Harris N (2009) Rainfall thresholding and susceptibility assessment of rainfall-induced landslides: Application to landslide management in St Thomas. Jamaica Bull Int Assoc Eng Geol 68:539–550

    Article  Google Scholar 

  • Min D-H, Yoon H-K (2021) Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility map**. Sci Rep 11:6594

    Article  Google Scholar 

  • Moore ID, Wilson JP (1992) Length-slope factors for the revised universal soil loss equation: simplified method of estimation. J Soil Water Conservation 47(5):423–428

    Google Scholar 

  • Moosavi V, Niazi Y (2016) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility map**. Landslides 13(1):97–114

  • Nam K, Wang F (2020) An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture. Japan Geoenviron Disasters 7:6

    Article  Google Scholar 

  • Negi HS, Kumar A, Rao NN, Thakur NK, Shekhar MS (2020) Susceptibility assessment of rainfall induced debris flow zones in Ladakh-Nubra region Indian Himalaya. J Earth Syst Sci 129(1):1–20

    Article  Google Scholar 

  • Nicu IC (2018) Application of analytic hierarchy process, frequency ratio, and statistical index to landslide susceptibility: an approach to endangered cultural heritage. Environ Earth Sci 77(3):79

    Article  Google Scholar 

  • Ngo PTT, Panahi M, Khosravi K, Ghorbanzadeh O, Kariminejad N, Cerda A, Lee S (2021) Evaluation of deep learning algorithms for national scale landslide susceptibility map** of Iran. Geosci Front 12(2):505–519

    Article  Google Scholar 

  • Nhu VH, Shirzadi A, Shahabi H, Chen W, Clague JJ, Geertsema M, Jaafari A, Avand M, Miraki S, Talebpour Asl D, Pham BT (2020) Shallow landslide susceptibility map** by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests 11(4):421

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Park S, Kim J (2019) Landslide susceptibility map** based on random forest and boosted regression tree models, and a comparison of their performance. Appl Sci 9:942. https://doi.org/10.3390/app9050942

    Article  Google Scholar 

  • Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Koppen € Geiger climate classification. Hydrology Earth Syst Sci 11:16331644

    Article  Google Scholar 

  • Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2):2–10

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia M (2016) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS Nat. Hazards, 83, 97-127

    Article  Google Scholar 

  • Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT, Le TM, Van Phong T, Khoi DK, Shirzadi A, Bui DT (2020a) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int 35(12):1267–1292

    Article  Google Scholar 

  • Pham VD, Nguyen QH, Nguyen HD, Pham VM, Bui QT (2020b) Convolutional neural network-optimized moth flame algorithm for shallow landslide susceptible analysis IEEE. Access 8:32727–32736

    Article  Google Scholar 

  • Phong TV, Ly H-B, Trinh PT, Prakash I, Hoan DT (2020) Landslide susceptibility map** using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach. Vietnam J Earth Sci 42(3):237–246

    Google Scholar 

  • Pisano L, Zumpano V, Malek Ž, Rosskopf CM, Parise M (2017) Variations in the susceptibility to landslides, as a consequence of landcover changes: a look to the past, and another towards the future. Sci Total Environ 601–602:1147–1159

    Article  Google Scholar 

  • Pishvaei MH, Sabzevari T, Noroozpour S, Mohammadpour R (2020) Effects of hillslope geometry on spatial infiltration using the TOPMODEL and SCS-CN models. Hydrol Sci J 65(2):212–226

    Article  Google Scholar 

  • Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA 162:177–192

    Article  Google Scholar 

  • Prakash N, Manconi A, Loew S (2021) A new strategy to map landslides with a generalized convolutional neural network. Sci Rep 11:9722

    Article  Google Scholar 

  • Prinz WC (1984) Geologic map of wadi Haliy quadrangle, sheet 19E, Kingdom of Saudi Arabia: Saudi Arabian Deputy Ministry for Mineral Resources Geoscience map GM-74 A, C, Scale 1:250000.

  • Qingfeng H, Zhihao X, Shaojun L, Renwei L, Shuai Z, Nianqin W, Pham BT, Wei C (2019) Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling. Entropy 21(2):106

    Article  Google Scholar 

  • Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh RMA, Shufeng T (2019) Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis. Earth Syst Environ 3:585–601

    Article  Google Scholar 

  • Rajesh BV, Kerle SN, Jetten V, Abdellah L, Machmach I (2015) Quantifying temporal changes in gully erosion areas with object-oriented analysis. CATENA 128:262–277. https://doi.org/10.1016/j.catena.2014.01.010

    Article  Google Scholar 

  • Ratte JC, Andreasen GF (1974) Reconnaissance geology and magnetic intensity map of the Jabal Sawdah Quadrangle, Kingdom of Saudi Arabia. Geologic map GM-16. Sheet 18/42C.

  • Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; Institute of Electrical and Electronics Engineers: Columbus, OH, USA, 2014; pp. 806–813

  • Roback K, Clark MK, West AJ, Zekkos D, Li G, Gallen SF, Chamlagain D, Godt JW (2018) The size, distribution, and mobility of landslides caused by the 2015 mw7.8 gorkha earthquake. Nepal Geomorphology 301:121–138

    Article  Google Scholar 

  • Roccati A, Paliaga G, Luino F, Faccini F, Turconi L (2021) GIS-Based Landslide Susceptibility Map** for Land Use Planning and Risk Assessment. Land 10:162

    Article  Google Scholar 

  • Roy J, Saha S, Arabameri A, Blaschke T (2019) Bui DT (2019) A Novel ensemble approach for landslide susceptibility map** (LSM) in Darjeeling and Kalimpong Districts, West Bengal. India Remote Sens 11:2866

    Article  Google Scholar 

  • Rwanga SS, Ndambuki JM (2017) Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int J Geosci 8:611–622

    Article  Google Scholar 

  • Saha S, Sarkar R, Roy J, Hembram TK, Acharya S, Thapa G (2021) Drukpa D (2021) Measuring landslide vulnerability status of Chukha. Bhutan Using Deep Learning Algorithms, Sci Rep 11:16374

    Google Scholar 

  • Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2:160

    Article  Google Scholar 

  • Shano L, Raghuvanshi TK, Meten M (2020) Landslide susceptibility evaluation and hazard zonation techniques - a review. Geoenviron Disasters 7:18

    Article  Google Scholar 

  • Sharma N, Sharma R, **dal N (2021) Machine Learning and deep learning applications-a vision. Glob Transit Proc 2(1):24–28

    Article  Google Scholar 

  • Sharon D (1972) The spottiness of rainfall in a desert area. J Hydro 17:161–175

    Article  Google Scholar 

  • Shu H, Hürlimann M, Molowny-Horas R, González M, Pinyol J, Abancó C, Ma J (2019) Relation between land cover and landslide susceptibility in Val d’Aran, Pyrenees (Spain): historical aspects, present situation and forward prediction. Sci Total Environ 693:133557

  • Sidle RC, Al-Shaibani AM (2018) Kaka SI (2018) Geomorphic hazards in south-west Saudi Arabia: The human–environmental nexus. Area 00:1–11. https://doi.org/10.1111/area.12509

    Article  Google Scholar 

  • Singh KK, Mehrotra A, Pal K (2014) Landslide detection from satellite images using spectral indices and digital elevation model. Disaster Adv 7:25–32

    Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR) 1–9

  • Tekin S (2021) Completeness of landslide inventory and landslide susceptibility map** using logistic regression method in Ceyhan Watershed (southern Turkey). Arab J Geosci 14:1706

    Article  Google Scholar 

  • Torcivia CEG, López NNR (2020) Preliminary Morphometric Analysis: Río Talacasto Basin, Central Precordillera of San Juan, Argentina. In: Collantes M., Perucca L., Niz A., Rabassa J. (eds) Advances in Geomorphology and Quaternary Studies in Argentina Springer Earth Syst Sci. Springer.

  • Tran QC, Minh DD, Jaafari A (2020) Novel ensemble landslide predictive models based on the hyperpipes algorithm: a case study in the nam dam commune. Vietnam Appl Sci 10(11):3710

    Article  Google Scholar 

  • Trigila A, Iadanza C, Spizzichino D (2010) Quality assessment of the Italian landslide inventory using GIS processing. Landslides 7:455–470

    Article  Google Scholar 

  • Turner AK (2018) Social and environmental impacts of landslides. Innov Infrastructure Solut 3:70

    Article  Google Scholar 

  • Van Den Eeckhaut M, Kerle N, Poesen J, Hervás J (2012) Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data. Geomorphology 173:30–42

    Article  Google Scholar 

  • van Westen CJ, Castellanos Abella EA, Sekhar LK (2008) Spatial data for landslide susceptibility, hazards and vulnerability assessment: an overview. Eng Geol 102:112–131

    Article  Google Scholar 

  • Wang H, Zhang L, Yin K, Luo H, Li J (2021) Landslide identification using machine learning. Geosci Front 12(1):351–364

    Article  Google Scholar 

  • Wang Q, Wang Y, Niu R, Peng L (2017) Integration of information theory, K-means cluster analysis and the logistic regression model for landslide susceptibility map** in the Three Gorges Area China. Remote Sens 9(9):938

    Article  Google Scholar 

  • Wang Y, Fang Z, Hong H (2019) Comparison of convolutional neural networks for landslide susceptibility map** in Yanshan County, China. Sci Total Environ 666:975–993

    Article  Google Scholar 

  • Wilson JP (1986) Estimating the topographic factor in the universal soil loss equation for watersheds. J. Soil and Water Cons. 41: 179-184.

  • Wooldridge JM (2015) Introductory econometrics. A modern approach. Cengage Learning, Boston, MA

    Google Scholar 

  • **ao T, Yin K, Yao T, Liu S (2019) Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County Three Gorges Reservoir, China. Acta Geochim 38:654–669

    Article  Google Scholar 

  • ** using an integrated machine learning model: a case study in the Lvliang Mountains of China. Front Earth Sci 9:722491

  • Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629

    Article  Google Scholar 

  • Yu J, Liu Q (2020) Larix olgensis growth-climate response between lower and upper elevation limits: an intensive study along the eastern slope of the Changbai mountains, northeastern China. J For Res 31(1):231–244

    Article  Google Scholar 

  • Yu H, Ma Y, Wang L, Zhai Y, Wang X (2017) A landslide intelligent detection method based on CNN and rsg_r. In Proceedings of the 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, 6–9 August 2017, 40–44.

  • Zhao Y, Han Q, Zhao Y, Liu J (2019) Soil pore identification with the adaptive fuzzy C-means method based on computed tomography images. J for Res 30(3):1043–1052

    Article  Google Scholar 

  • Zhao S, Zhao Z (2021) A Comparative Study of Landslide Susceptibility Map** Using SVM and PSO-SVM Models Based on Grid and Slope Units. Hindawi Math Probl Eng 8854606.

  • Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area China. Comput Geosci 112:23–37

    Article  Google Scholar 

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Authors and Affiliations

  1. Geology Department, Faculty of Science, Sohag, Sohag University, Sohag, Egypt

    Ahmed M. Youssef

  2. Geological Hazards Center, Saudi Geological Survey, P.O. Box 54141, Jeddah, 21514, Kingdom of Saudi Arabia

    Ahmed M. Youssef & Mohamed M. Al-Katheri

  3. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering & IT, University of Technology Sydney, NSW 2007, Sydney, Australia

    Biswajeet Pradhan & Abhirup Dikshit

  4. Center of Excellence for Climate Change Research, King Abdulaziz University, P. O. Box 80234, Jeddah 21589, Saudi Arabia

    Biswajeet Pradhan

  5. Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia

    Biswajeet Pradhan

  6. Remote Sensing Department, Saudi Geological Survey (SGS), P.O. Box 54141, Jeddah 21514, Saudi Arabia

    Saleh S. Matar

  7. Geology Department, Faculty of Science, South Valley University, 83523, Qena, Egypt

    Ali M. Mahdi

Authors
  1. Ahmed M. Youssef
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  2. Biswajeet Pradhan
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  3. Abhirup Dikshit
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  4. Mohamed M. Al-Katheri
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  5. Saleh S. Matar
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  6. Ali M. Mahdi
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Correspondence to Ahmed M. Youssef.

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Youssef, A.M., Pradhan, B., Dikshit, A. et al. Landslide susceptibility map** using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA. Bull Eng Geol Environ 81, 165 (2022). https://doi.org/10.1007/s10064-022-02657-4

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  • DOI: https://doi.org/10.1007/s10064-022-02657-4

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