Abstract
Recently, one of the most frequent natural hazards around several regions in the world is the landslide events. The area of Jabal Farasan in the northwest Jeddah of Saudi Arabia suffers from landslide events. The main cause of these events was identified due to the anthropogenic activities represented by mining activities. In this work, different machine learning algorithms (MLA), Random Forest (RF), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) were implemented to predict the landslides events integrated with remote sensing data. The objective is to generate landslides susceptibility prediction map using different MLA. The landslides inventory map was prepared in the present study based on the historical landslide’s events. Landslides at 1354 locations were used to train the models and validate the prediction. Landslides controlling factors include: elevation, slope, curvature, aspect, distance from roads, lineaments density, and topographic wetness index (TWI). Our findings indicate that landslides more likely occurs in the areas of mining activities, close to the roads and the Wadi tributaries due to the high slope angle in some cases. Subsequently, the prediction maps were classified into landslide occurrence location and non-landslides occurrence’s model's validation using the receiver operating characteristic (ROC) curve showed that the model accuracy varied between 86 and 89% for RF, KNN, and NB. The produced landslide susceptibility map in this study would provide useful information for hazard management and control in such natural hazards.
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The data that support the findings of this study are available from the corresponding author, upon reasonable request.
References
Abu El-Magd SA (2019) Flash flood hazard mapping using GIS and bivariate statistical method at wadi Bada’a, Gulf of Suez. Egypt J Geosci Environ Prot 7:372–385
Abu El-Magd SA, Amer RA, Embaby A (2020) Multi-criteria decision-making for the analysis of flash floods: A case study of Awlad Toq-Sherq, Southeast Sohag, Egypt. J African Earth Sci 162:103709
Abu El-Magd SA, Pradhan B, Alamri A (2021) Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert. Egypt Arabian Journal of Geosciences 14:323. https://doi.org/10.1007/s12517-021-06466-z
Achour Y, Pourghasemi HR (2020) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front 11:871–883. https://doi.org/10.1016/j.gsf.2019.10.001
Aditian A, Kubota T, Shinohara Y (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
Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Linh NTT, Nguyen HQ, Ahmad A, Ghorbani MA (2020a) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin. Slovakia Ecological Indicators 115:106620. https://doi.org/10.1016/j.ecolind.2020.106620
Ali SA, Parvin F, Vojteková J, Costache R, Linh NTT, Pham QB, Vojtek M, Gigović L, Ahmad A, Ghorbani MA (2020b) GIS-Based Landslide Susceptibility Modeling: A Comparison between Fuzzy Multi-Criteria and Machine Learning Algorithms. Geosci Front 12:857–876. https://doi.org/10.1016/j.gsf.2020.09.004
Anbalagan R, Kumar R, Lakshmanan K, Parida S, Neethu S (2015) Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley. Geoenvironmental Disaster, Sikkim. https://doi.org/10.1186/s40677-014-0009-y
Arabameri A, Pradhan B, Rezaei K, Sohrabi M, Kalantari Z (2019) GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci 16:595–618
Balogun AL, Rezaie F, Pham QB, Gigović L, Drobnjak S, Aina YA, ... Lee S (2021) Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO. BAT and COA algorithms. Geosci Front 12(3):101104
Bloom AL (1998) Geomorphology: a systematic analysis of late Cenozoic landforms (No 551.41 B5.). Prentice Hall, Upper Saddle River, New Jersey
Calle ML, Urrea V (2010) Letter to the Editor: stability of random forest importance measures. Briefings Bioinf 12(1):86–89. https://doi.org/10.1093/bib/bbq011
Cao, J., Zhang, Z., Du, J., Zhang, L., Song, Y., Sun, G. (2020). Multi-geohazards susceptibility mapping based on machine learning - a case study in Jiuzhaigou, China. Natural Hazards, https://doi.org/10.1007/s11069-020-03927-8
Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017a) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85
Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H (2017b) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, NaïveBayes tree, and alternating decision tree models. Geomat Nat Haz Risk 8(2):950–973
Chen W, Xie X, Wang J, Pradhan B, Hong H, Tien Bui D, Duan Z, Ma J (2017c) 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. https://doi.org/10.1016/j.catena.2016.11.032
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 naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ 644:1006–1018
Das S, Raja D (2015) Susceptibility analysis of landslide in Chittagong City Corporation Area, Bangladesh. Int J Environ 4(2):157–181. https://doi.org/10.3126/ije.v4i2.12635
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility mapping 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(1):135–165
Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29:103–130
Dubey CS, Chaudhry M, Sharma BK, Pandey AC, Singh B (2005) Visualization of 3-D digital elevation model for landslide assessment and prediction in mountainous terrain: A case study of Chandmari landslide Sikkim, eastern Himalayas. Geosci J 9(4):363–373
Fang Z, Wang Y, Peng L, Hong H (2020) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci. https://doi.org/10.1080/13658816.2020.1808897
Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11
Ghimire M (2001) Geo-hydrological hazard and risk zonation of Banganga watershed using GIS and remote sensing. Journal of Nepal Geological Society 23:99–110
Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81(1):65–83. https://doi.org/10.1016/j.enggeo.2005.07.011
Gordo C, Zezere JL, Marques R (2019) Landslide susceptibility assessment at the basin scale for rainfall- and earthquake-triggered shallow slides. Geosciences 9:268
Hassangavyar MB, Damaneh HE, Pham QB, Linh NTT, Tiefenbacher J, Bach QV (2020) Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility. Geocarto Int 1–23
Hobbs WH (1904) Lineaments of the Atlantic border region. Bulletin of the Geological Society of America 15(1):483–506
Hong H, Pourghasemi HR, Z., Pourtaghi, S. (2016) Landslide Susceptibility Assessment in Lianhua County (china): a Comparison between a Random Forest Data Mining Technique and Bivariate and Multivariate Statistical Models. Geomorphology 259:105–118. https://doi.org/10.1016/j.geomorph.2016.02.012
Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu A-X, Chen W, Ahmad BB (2018) Landslide susceptibility mapping using J48 decision tree with ADAboost, bagging and rotation forest ensembles in the Guangchang area (China). CATENA 163:399–413
Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics. Nat Hazards Risk 9:49–69
Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408. https://doi.org/10.1016/j.cageo.2012.11.003
Maerz H, N., Youssef, A.M., Pradhan, B. and Bulkhi, A. (2014) Remediation and mitigation strategies for rock fall hazards along the highways of Fayfa Mountain, Jazan Region, Kingdom of Saudi. Arab J Geosci 8:2633–2651. https://doi.org/10.1007/s12517-014-1423-x
Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234
Mezaal MR, Pradhan B, Sameen MI, Mohd Shafri HZ, Yusoff ZM (2017) Optimized Neural Architecture for Automatic Landslide Detection from High- Resolution Airborne Laser Scanning Data. Appl Sci 7:730. https://doi.org/10.3390/app7070730
Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46:33–57. https://doi.org/10.1007/s11004-013-9511-0
Moosavi V, Niazi Y (2016) Development of Hybrid Wavelet Packet-statistical Models (WP-SM) for Landslide Susceptibility Mapping. Landslides 13(1):97–114. https://doi.org/10.1007/s10346-014-0547-0
Nhu V-H, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, Clague JJ, Jaafari A, Chen W, Nguyen H (2020) Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. Int J Environ Res Public Health 17(14):4933. https://doi.org/10.3390/ijerph17144933
Nsengiyumva JB, Luo G, Amanambu AC, Mind’je R, Habiyaremye G, Karamage F, Ochege FU, Mupenzi C (2019) Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Sci Total Environ 659:1457–1472
Pathak D (2016) Knowledge based landslide susceptibility mapping in the Himalayas. Geoenvironmental Disasters 3(1):1–11
Petley DN (2008) The global occurrence of fatal landslides in 2007. Geophysical Research Abstracts, vol 10. EGU General Assembly, p 3
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 122(3–4):1–19
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia M (2017) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128:255–273
Pham QB, Yang TC, Kuo CM, Tseng HW, Yu PS (2019) Combing random forest and least square support vector regression for improving extreme rainfall downscaling. Water 11(3):451
Pham QB, Mukherjee K, Norouzi A, Linh NTT, Janizadeh S, Ahmadi K, Anh DT (2020) Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran. Geomatics Nat Hazards Risk 11(1):2385–2410
Pokharel B, Althuwaynee OF, Aydda A, Kim SW, Lim S, Park HJ (2021) Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal. Landslides 18(4):1403–1419
Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA 162:177–192. https://doi.org/10.1016/j.catena.2017.11.022
Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using backpropagation neural networks model at Cameron Highland. Malaysia Landslides 7(1):13–30. https://doi.org/10.1007/s10346-009-0183-2
Quevedo RP, Guasselli LA, de Oliveira GG, Ruiz LFC (2020) Modelagem de áreas suscetíveis a movimentos de massa: avaliação comparativa de técnicas de amostragem, aprendizado de máquina e modelos digitais de elevação. Geociências (São Paulo) 38(3):781–795
Rabby YW, Hossain MB, Abedin J (2020) Landslide susceptibility mapping in three upazilas of Rangamati Hill District Bangladesh: application and comparison of gis-based machine learning methods. Geocarto Int 1–24
Rebala G, Ravi A, Churiwala S (2019) 2019. Springer International Publishing, An Introduction to Machine Learning
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
Roccati A, Faccini F, Luino F, Ciampalini A, Turconi L, Roccati A, Faccini F, Luino F, Ciampalini A, Turconi L (2019) Heavy rainfall triggering shallow landslides: a susceptibility assessment by a GIS-approach in a Ligurian Apennine catchment (Italy). Water 11:605
Sachdeva S, Bhatia T, Verma AK (2020) A novel voting ensemble model for spatial prediction of landslides using GIS. Int J Remote Sens 41(3):929–952. https://doi.org/10.1080/01431161.2019.1654141
Sangchini EK, Nowjavan MR, Arami A (2015) Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran. J Fac For Istanbul Univ 65(1):30–40. https://doi.org/10.17099/jffiu.52751
Shahabi H, Hashim M, Ahmad BB (2015) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin. Iran Environ Earth Sci 73:8647. https://doi.org/10.1007/s12665-015-4028-0
Souza FT, Ebecken NFF (2004) A data mining approach to landslide prediction. WIT Transactions on Information and Communication Technologies 33
Strupler M, Danciu L, Hilbe M, Kremer K, Anselmetti FS, Strasser M, Wiemer S (2018) A subaqueous hazard map for earthquake-triggered landslides in Lake Zurich. Switzerland Nat Hazards 90:51–78
Taalab K, Cheng T, Zhang Y (2018) Mapping landslide susceptibility and types using Random Forest. Big Earth Data 2:1–20
Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136
Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena 145:164–179
Van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation -why is it still so difficult? Bull Eng Geol Environ 65:167–184. https://doi.org/10.1007/s10064-005-0023-0
Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. UNESCO, Paris
Vojteková J, Vojtek M (2019) GIS-Based Landscape Stability Analysis: A Comparison of Overlay Method and Fuzzy Model for the Case Study in Slovakia. Prof Geogr 71(4):631–644
Vojteková J, Vojtek M (2020) Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia. Geomat Nat Haz Risk 11(1):131–148
Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, ... Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37
Xiao T, Yin K, Yao T, Liu S (2019) Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County. Acta Geochimica Three Gorges Reservoir, China. https://doi.org/10.1007/s11631-019-00341-1
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility Mapping in Trabzon, NE Turkey. CATENA 85:274–287
Youssef AM, Maerz N (2013) Overview of some geological hazards in the Saudi Arabia. Environ Earth Sci 70:3115–3130. https://doi.org/10.1007/s12665-013-2373-4
Youssef AM, Al-kathery M, Pradhan B (2014) Landslide susceptibility mapping at AlHasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J https://doi.org/10.1007/s12303-014-0032-8
Youssef AM, Al-kathery M, Pradhan B, Elsahly T (2016) Debris flow impact assessment along the Al-Raith Road, Kingdom of Saudi Arabia, using remote sensing data and field investigations. Geomatics. Nat Hazards Risk 7:620–638. https://doi.org/10.1080/19475705.2014.933130
Youssef AM, Maerz HN, Al-Otaibi AA (2012) Stability of rock slopes along Raidah Escarpment road, Asir area, Kingdom of Saudi Arabia J Geogr https://doi.org/10.5539/jgg.v4n2p48
Youssef M, Pourghasemi HR (2021) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region. Saudi Arabia Geoscience Frontiers 12(2021):639–655
Yusof N, Ramli MF, Pirasteh S, Shafri HZM (2011) Landslides and lineament mapping along the Simpang Pulai to Kg Raja highway. Malaysia International Journal of Remote Sensing 32(14):4089–4105
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Sherif Ahmed Abu El-Magd: Conceptualization, Writing- original draft, Software, Formal analysis, Visualization.
Sk Ajim Ali: Formal analysis; Writing- original draft, Visualization. Quoc Bao Pham: Writing, Review and editing, Supervision.
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Abu El-Magd, S.A., Ali, S.A. & Pham, Q.B. Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Sci Inform 14, 1227–1243 (2021). https://doi.org/10.1007/s12145-021-00653-y
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DOI: https://doi.org/10.1007/s12145-021-00653-y