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Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping

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Abstract

Two Artificial Intelligence (AI) methods, Fuzzy Inference System (FIS) and Artificial Neural Network (ANN), are applied to Landslide Susceptibility Mapping (LSM), to compare complementary aspects of the potentials of the two methods and to extract physical relationships from data. An index is proposed in order to rank and filter the FIS rules, selecting a certain number of readable rules for further interpretation of the physical relationships among variables. The area of study is Rolante river basin, southern Brazil. Eleven attributes are generated from a Digital Elevation Model (DEM), and landslide scars from an extreme rainfall event are used. Average accuracy and area under Receiver Operating Characteristic curve (AUC) resulted, respectively, in 81.27% and 0.8886 for FIS, and 89.45% and 0.9409 for ANN. ANN provides a map with more amplitude of outputs and less area classified as high susceptibility. Among the 40 (10%) best-ranked FIS rules, 13 have high susceptibility output, while 27 have low; a cause is that low susceptibility areas are larger on the map. Slope is highly connected to susceptibility. Elevation, when high (plateau) or low (floodplain), inhibits high susceptibility. Six attributes show the same fuzzy set for the 18 best-ranked rules, meaning this fuzzy set is common on the map. Overall findings point out that ANN is best suited for LSM map generation, but, based on them, using FIS is important to help researchers understand more about AI models for LSM and about landslide phenomenon.

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Data availability

Digital elevation model used is available upon request to ASF DAAC. Software QGis is available at qgis.org.

Code availability

Custom codes developed on Matlab platform.

References

  • Arnone E, Francipane A, Scarbaci A et al (2016) Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping. Environ Model Softw 84:467–481

    Article  Google Scholar 

  • ASF DAAC (2018) ALOS PALSAR Radiometric Terrain Corrected High res

  • Benitez JM, Castro JL, Requena I (1997) Are Artificial Neural Networks Black Boxes? IEEE Trans Neural Netw 8:1156–1164

    Article  Google Scholar 

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Haz Earth Sys Sci 5(6):853–862

    Article  Google Scholar 

  • Cruden DM (1991) A simple definition of a landslide. Bull Eng Geol Environ 43:27–29

    Google Scholar 

  • DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  Google Scholar 

  • Dou J, Yunus AP, Tien Bui D et al (2019) Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens 11:638

    Article  Google Scholar 

  • Dourado F, Arraes TC, Fernandes M et al (2012) O Megadesastre da Região Serrana do Rio de Janeiro: as causas do evento, os mecanismos dos movimentos de massa e a distribuição espacial dos investimentos de reconstrução no pós-desastre. Anuário do Inst Geociências 35:43–54

    Google Scholar 

  • Driankov D, Hellendoorn H, Reinfrank M (Michael) (1996) An introduction to fuzzy control. Springer.

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

    Article  Google Scholar 

  • Frank HT, Gomes MEB, Formoso MLL (2009) Review of the areal extent and the volume of the Serra Geral Formation, Paraná Basin, South America. Pesqui em Geociências 36:49–57

    Article  Google Scholar 

  • Franzmeier DP, Pedersen EJ, Longwell TJ et al (1969) Properties of some soils in the cumberland plateau as related to slope aspect and position. Soil Sci Soc Am J 33:755–761. https://doi.org/10.2136/sssaj1969.03615995003300050037x

    Article  Google Scholar 

  • Guha-Sapir D (2019) EM-DAT: the emergency events database. Univ Cathol Louvain Brussels, Belgium

    Google Scholar 

  • Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2:359–366

    Article  Google Scholar 

  • Huffman GJ, Bolvin DT, Nelkin EJ et al (2007) The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55

    Article  Google Scholar 

  • Isard SA (1986) factors influencing soil moisture and plant community distribution on niwot ridge. Arct Alp Res 18:83–96. https://doi.org/10.1080/00040851.1986.12004065

    Article  Google Scholar 

  • Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366

    Article  Google Scholar 

  • Kosko B (1992) Neural networks and fuzzy systems : a dynamical systems approach to machine intelligence. Prentice Hall

  • Lee CC (1990a) fuzzy logic in control systems: fuzzy logic controller—part I. IEEE Trans Syst Man Cybern 20:404–418. https://doi.org/10.1109/21.52551

    Article  Google Scholar 

  • Lee CC (1990b) Fuzzy logic in control systems: fuzzy logic controller, part II. IEEE Trans Syst Man Cybern 20:419–435. https://doi.org/10.1109/21.52552

    Article  Google Scholar 

  • Lee S, Choi J, Woo I (2004) The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun. Korea Geosci J 8:51

    Article  Google Scholar 

  • Lucchese LV, de Oliveira GG, Pedrollo OC (2020) Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment. Environ Monit Assess 192:129. https://doi.org/10.1007/s10661-019-7968-0

    Article  Google Scholar 

  • Lucchese LV, de Oliveira GG, Pedrollo OC (2021) Investigation of the influence of nonoccurrence sampling on Landslide Susceptibility Assessment using Artificial Neural Networks. Catena 198:105067. https://doi.org/10.1016/j.catena.2020.105067

    Article  Google Scholar 

  • Mamdani EH (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput 26:1182–1191. https://doi.org/10.1109/TC.1977.1674779

    Article  Google Scholar 

  • Minsky M (1961) Steps Toward Artificial Intelligence. In: The Guest Editor (ed) Institute of Radio Engineers. IEEE

  • Murphy KP (2012) Machine Learning A Probabilistic Perspective. Massachusetts Institute of Technology

  • Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86:12–24. https://doi.org/10.1016/j.geomorph.2006.08.002

    Article  Google Scholar 

  • Peethambaran B, Anbalagan R, Shihabudheen KV (2019) Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system-a comparative study. Nat Hazards 96:121–147. https://doi.org/10.1007/s11069-018-3532-4

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed. Iran Nat Hazards 63:965–996. https://doi.org/10.1007/s11069-012-0217-2

    Article  Google Scholar 

  • Pradhan B (2010) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Intell Syst 3:370–381. https://doi.org/10.1080/18756891.2010.9727707

    Article  Google Scholar 

  • Pradhan B, Pirasteh S (2010) Comparison between Prediction Capabilities of Neural Network and Fuzzy Logic Techniques for Landslide Susceptibility Mapping

  • Rasmussen KL, Choi SL, Zuluaga MD, Houze RA (2013) TRMM precipitation bias in extreme storms in South America. Geophys Res Lett 40:3457–3461. https://doi.org/10.1002/grl.50651

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536

    Article  Google Scholar 

  • Secretaria Estadual do Meio Ambiente, Grupo de Pesquisa em Desastres Naturais (2017) Diagnóstico preliminar

  • SEN Z, (2010) Fuzzy logic and hydrological modeling. CRC Press, Taylor and Francis Group

    Google Scholar 

  • Serviço Geológico do Brasil - CPRM (2011) Atlas pluviométrico do Brasil: isoietas mensais, isoietas trimestrais, isoietas anuais, meses mais secos, meses mais chuvosos, trimestres mais secos, trimestres mais chuvosos. http://www.cprm.gov.br/publique/Hidrologia/Mapas-e-Publicacoes/Atlas-Pluviometrico-do-Brasil-1351.html. Accessed 19 Oct 2020

  • Turner S, Regelous M, Kelley S et al (1994) Magmatism and continental break-up in the South Atlantic: high precision 40Ar-39Ar geochronology. Earth Planet Sci Lett 121:333–348

    Article  Google Scholar 

  • Vogl TP, Mangis JK, Rigler AK et al (1988) Accelerating the convergence of the back-propagation method. Biol Cybern 59:257–263

    Article  Google Scholar 

  • Wang L-X (1992) Fuzzy systems are universal approximators. In: [1992 Proceedings] IEEE International Conference on Fuzzy Systems. pp 1163–1170

  • Wang L-X, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22:1414–1427

    Article  Google Scholar 

  • Wang L, Wei S, Horton R, Shao M (2011) Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China. CATENA 87:90–100. https://doi.org/10.1016/j.catena.2011.05.010

    Article  Google Scholar 

  • White IC (1908) Report on the coal measures and associated rocks of South Brazil. Comm Estud Minas Brazil Rio Janeiro

  • 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, THREE Gorges Reservoir, China. Acta Geochim 38:654–669

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy Sets. Inf. Control 8:338–353

    Article  Google Scholar 

  • Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern SMC 3:28–44. https://doi.org/10.1109/TSMC.1973.5408575

    Article  Google Scholar 

  • Zhu AX, Wang R, Qiao J et al (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138. https://doi.org/10.1016/j.geomorph.2014.02.003

    Article  Google Scholar 

Download references

Funding

This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) [Edital 01/2017–ARD, Grant Number 17/2551–0000894-4].

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Authors and Affiliations

  1. Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil

    Luísa Vieira Lucchese & Olavo Correa Pedrollo

  2. Departamento Interdisciplinar, Universidade Federal do Rio Grande do Sul, Rodovia RS 030, 11700, km 92. Emboaba, Tramandaí, RS, 95590-000, Brazil

    Guilherme Garcia de Oliveira

Authors
  1. Luísa Vieira Lucchese
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  2. Guilherme Garcia de Oliveira
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  3. Olavo Correa Pedrollo
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Contributions

OCP contributed to conceptualization; LVL and OCP contributed to methodology and software; GGDO contributed to validation and investigation; LVL contributed to writing—original draft and visualization; GGDO and OCP contributed to writing—review and editing and supervision.

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Correspondence to Luísa Vieira Lucchese.

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Lucchese, L.V., de Oliveira, G.G. & Pedrollo, O.C. Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping. Nat Hazards 106, 2381–2405 (2021). https://doi.org/10.1007/s11069-021-04547-6

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