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Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA

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Abstract

The present study is aimed at conducting a comparative landslide susceptibility assessment in a landslide-prone subset area of the Tajan Watershed in northern Iran. For this aim, three probabilistic models were used namely: multilayer perceptron Artificial Neural Networks with a Back-Propagation algorithm (BPANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the coupled ANFIS-Imperialist Competitive Algorithm (ANFIS-ICA). An all-inclusive landslide inventory map was prepared together with ten pivotal geo-environmental and anthropogenic landslide-controlling factors. Three indices including Pierce Skill Score (PSS), Cohen’s kappa, and the Area Under the Receiver Operating Characteristic curve (AUROC) were calculated from the confusion matrix and used to assess the performance of the models. Results in the validation stage revealed that the ensemble of ANFIS-ICA outperformed its counterparts with the respective PSS, kappa, and AUROC values of 0.766, 0.792, 0.966, followed by ANFIS (0.629, 0.666, 0.902), and ANN (0.603, 0.652, 0.866). Regarding the superior model (ANFIS-ICA), about 27% of the study area falls within high landslide susceptibility zones which needs to be considered for further risk mitigation measures and pragmatic actions. Our results pinpointed the outstanding performance of ANFIS-ICA ensemble in landslide susceptibility modeling at the watershed scale. Furthermore, it was found that the ANFIS-ICA model borrows most of its susceptibility pattern and performance from the distance to roads factor, although the total performance of the model is derived from the integration of all the factors. Moreover, this study attested to the advantages of hybrid algorithms and showed that the integration of machine learning models with evolutionary algorithms can be a new horizon to ensemble modeling. Utilization of well-adjusted ensemble models is pivotal for natural resource managers due particularly to their enhanced prediction powers which, in turn, can significantly reduce the social-economic losses emanated from failed predictions.

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Notes

  1. Forest, Range and Watershed Management Organization.

  2. Forest, Range and Watershed Management Organization.

References

  • Adeodato PJ, Arnaud AL, Vasconcelos GC, Cunha RC, Monteiro DS (2011) MLP ensembles improve long term prediction accuracy over single networks. Int J Forecast 27(3):661–671

    Google Scholar 

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9(1):93–106

    Google Scholar 

  • Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232

    Google Scholar 

  • Arabameri A, Pourghasemi HR, Yamani M (2017) Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ Earth Sci 76(24):832

    Google Scholar 

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, IEEE, pp 4661–4667

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31

    Google Scholar 

  • Azarafza M, Ghazifard A, Akgün H, Asghari-Kaljahi E (2018) Landslide susceptibility assessment of South Pars Special Zone, southwest Iran. Environ Earth Sci 77(24):805

    Google Scholar 

  • Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3–31

    Google Scholar 

  • Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG (2012) Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters. Environ Earth Sci 66(2):537–548

    Google Scholar 

  • Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B (2017) Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ 575:119–134

    Google Scholar 

  • Behrooz F, Mariun N, Marhaban M, Mohd Radzi M, Ramli A (2018) Review of control techniques for HVAC systems—nonlinearity approaches based on Fuzzy cognitive maps. Energies 11(3):495

    Google Scholar 

  • Berberian M (1981) Active faulting and tectonics of Iran. Zagros Hindu Kush Himal Geodyn Evol 3:33–69

    Google Scholar 

  • Bhat IA, Shafiq M, Ahmed P, Kanth TA (2019) Multi-criteria evaluation for landslide hazard zonation by integrating remote sensing, GIS and field data in North Kashmir Himalayas, J&K, India. Environ Earth Sci 78(20):613

    Google Scholar 

  • Böhner J, Selige T (2006) Spatial prediction of soil attributes using terrain analysis and climate regionalisation. SAGA—analysis and modelling applications. Göttinger Geographische Abhandlungen, Göttingen, pp 13–28

    Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211

    Google Scholar 

  • Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017) 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

    Google Scholar 

  • Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Duan Z, Li S, Zhu AX (2018) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. CATENA 164:135–149

    Google Scholar 

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

    Google Scholar 

  • Cherkassky V, Krasnopolsky V, Solomatine DP, Valdes J (2006) Computational intelligence in earth sciences and environmental applications: Issues and challenges. Neural Netw 19(2):113–121

    Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Google Scholar 

  • Cruden DM, Varnes DJ (1996) Landslide types and processes. Landslides: investigation and mitigation, special report 247. Transportation Research Board, Washington, pp 36–75

    Google Scholar 

  • Dehghani GA, Makris J (1983) The gravity field and crustal structure of Iran. Neues Jahrbuch für Geologie und Paläontologie 168:215–229

    Google Scholar 

  • Eker AM, Dikmen M, Cambazoğlu S, Düzgün ŞH, Akgün H (2012) Application of artificial neural network and logistic regression methods to landslide susceptibility mapping and comparison of the results for the Ulus district, Bartın. J Fac Eng Archit Gazi Univ 27(1):163–173

    Google Scholar 

  • Eker AM, Dikmen M, Cambazoğlu S, Düzgün ŞH, Akgün H (2015) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey. Int J Geogr Inf Sci 29(1):132–158

    Google Scholar 

  • Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72

    Google Scholar 

  • Geological Survey Department of Iran (GSDI) (1997) The geological quadrangle maps of Pol-e Sefid and Sari at 1:100,000 scale https://gsi.ir/fa/map/263/-%D9%BE%D9%84-%D8%B3%D9%81%DB%8C%D8%AFhttps://gsi.ir/fa/map/726/-%D8%B3%D8%A7%D8%B1%D9%89

  • Govindaraju RS, Rao AR (eds) (2013) Artificial neural networks in hydrology, vol 36. Springer, Berlin

    Google Scholar 

  • Graupe D (2013) Principles of artificial neural networks—advanced series on circuits and systems, vol 3, 3rd edn. World Scientific Press, Singapore, p 384

    Google Scholar 

  • Guo Q, Liu Y (2010) ModEco: an integrated software package for ecological niche modeling. Ecography 33(4):637–642

    Google Scholar 

  • Gurney K (2014) An introduction to neural networks. CRC Press, Cambridge

    Google Scholar 

  • Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international conference on Neural Networks. IEEE Press, New York, 3:11–14.

  • Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu AX, 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

    Google Scholar 

  • Hosseini S, Al Khaled A (2014) A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl Soft Comput 24:1078–1094

    Google Scholar 

  • Jang JSR (1991) Fuzzy modeling using generalized neural networks and kalman filter algorithm. Assoc Adv Artif Intell 91:762–767

    Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Google Scholar 

  • Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence. IEEE Trans Autom Control 42(10):1482–1484

    Google Scholar 

  • 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). Geomat Nat Hazards Risk 9(1):49–69

    Google Scholar 

  • Kim JC, Lee S, Jung HS, Lee S (2018) Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int 33(9):1000–1015

    Google Scholar 

  • Kornejady A, Ownegh M, Bahremand A (2017) Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. CATENA 152:144–162

    Google Scholar 

  • Kornejady A, Ownegh M, Rahmati O, Bahremand A (2018) Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND. Geocarto Int 33(11):1155–1185

    Google Scholar 

  • Mahdavian A, Ghorashi M (2008) Seismotectonics and seismic hazard assessment of the sari quadrangle in Iran. In: the 14th World conference on earthquake engineering, October 12–17, 2008, Beijing, China.

  • Mazandaran Regional Water Authority (2018) Excel-worksheet: rainfall data of the raingauge stations in the Mazandaran Province.

  • Mazandaran’s FRWO office (2018) Land use/cover thematic map archive at 1:100,000 scale.

  • McCullough WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–127

    Google Scholar 

  • McKenzie D (1972) Active tectonics of the Mediterranean region. Geophys J Int 30(2):109–185

    Google Scholar 

  • Mollaiy Berneti S (2013) A hybrid approach based on the combination of adaptive neuro-fuzzy inference system and imperialist competitive algorithm: oil flow rate of the wells prediction case study. Int J Comput Intell Syst 6(2):198–208

    Google Scholar 

  • Nazari-Shirkouhi S, Eivazy H, Ghodsi R, Rezaie K, Atashpaz-Gargari E (2010) Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm. Expert Syst Appl 37(12):7615–7626

    Google Scholar 

  • O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690

    Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276

    Google Scholar 

  • Okalp K, Akgün H (2016) National level landslide susceptibility assessment of Turkey utilizing public domain dataset. Environ Earth Sci 75(9):847

    Google Scholar 

  • Peirce CS (1884) The numerical measure of the success of predictions. Science 93:453–454

    Google Scholar 

  • Polykretis C, Chalkias C, Ferentinou M (2019) Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area. Bull Eng Geol Environ 78(2):1173–1187

    Google Scholar 

  • Pontius RG Jr, Schneider LC (2001) Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85(1–3):239–248

    Google Scholar 

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

    Google Scholar 

  • Pourghasemi HR, Rossi M (2017) Landslide susceptibility modeling in a landslide prone area in Mazandaran Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 130(1–2):609–633

    Google Scholar 

  • Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177

    Google Scholar 

  • Rahmati O, Kornejady A, Samadi M, Nobre AD, Melesse AM (2018) Development of an automated GIS tool for reproducing the HAND terrain model. Environ Model Softw 102:1–12

    Google Scholar 

  • Rahmati O, Kornejady A, Samadi M, Deo RC, Conoscenti C, Lombardo L, Dayal K, Taghizadeh-Mehrjardi R, Pourghasemi HR, Kumar S, Bui DT (2019) PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches. Sci Total Environ 664:296–311

    Google Scholar 

  • Rao RV (2016) Teaching-learning-based optimization algorithm. teaching learning based optimization algorithm. Springer, Cham, pp 9–39

    Google Scholar 

  • Rozos D, Bathrellos GD, Skillodimou HD (2011) Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environ Earth Sci 63(1):49–63

    Google Scholar 

  • Schunk RW, Scherliess L, Eccles V, Gardner LC, Sojka JJ, Zhu L, Pi X, Mannucci AJ, Wilson BD, Komjathy A, Wang C, Rosen G (2014) Ensemble modeling with data assimilation models: a new strategy for space weather specifications, forecasts, and science. Space Weather 12(3):123–126

    Google Scholar 

  • Sevgen E, Kocaman S, Nefeslioglu HA, Gokceoglu C (2019) A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest. Sensors 19(18):3940

    Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219

    Google Scholar 

  • Singh AK (2010) Landslide management: concept and philosophy. Disaster Prev Manag Int J 19(1):119–134

    Google Scholar 

  • Skilodimou HD, Bathrellos GD, Chousianitis K, Youssef AM, Pradhan B (2019) Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environ Earth Sci 78(2):47

    Google Scholar 

  • Stoecklin J (1968) Structural history and tectonics of Iran: a review. AAPG Bull 52(7):1229–1258

    Google Scholar 

  • Tsangaratos P, Loupasakis C, Nikolakopoulos K, Angelitsa V, Ilia I (2018) Developing a landslide susceptibility map based on remote sensing, fuzzy logic and expert knowledge of the Island of Lefkada, Greece. Environ Earth Sci 77(10):363

    Google Scholar 

  • Van Westen CJ, Van Asch TW, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65(2):167–184

    Google Scholar 

  • Werbos P (1974) Beyond regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Ph. D. dissertation, Harvard University.

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836

    Google Scholar 

  • Youssef AM (2015) Landslide susceptibility delineation in the Ar-Rayth area, Jizan, Kingdom of Saudi Arabia, using analytical hierarchy process, frequency ratio, and logistic regression models. Environ Earth Sci 73(12):8499–8518

    Google Scholar 

  • Zurada JM (1992) Introduction to artificial neural systems, vol 8. West publishing company, St. Paul

    Google Scholar 

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  1. Department of Forest, Range and Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mehdi Sadighi, Baharak Motamedvaziri, Hasan Ahmadi & Abolfazl Moeini

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  1. Mehdi Sadighi
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  2. Baharak Motamedvaziri
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Correspondence to Baharak Motamedvaziri.

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Sadighi, M., Motamedvaziri, B., Ahmadi, H. et al. Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA. Environ Earth Sci 79, 536 (2020). https://doi.org/10.1007/s12665-020-09294-8

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