Abstract
This research examines the potential of spatial prediction of landslide susceptibility by implementing an evolutionary approach using symbolic classification with genetic programming (GP). Specifically, the light detection and ranging (LiDAR)-based digital elevation model was used to generate topographic prediction attributes and to digitize the location of shallow landslides by derivatives such as hillshade maps and contours. The presented approach tested a total of 72 runs with different parameter configurations for producing a good outcome among a number of possible solutions by varying population size, tournament group size and mutation probability. The final solution depicted a total of three important variables including slope, wetness index and solar insulation that were used in the prediction. The GP methodology used symbolic expression trees for the development of the predictive models that were tested and validated in the northern portion of the Cuyahoga Valley National Park located in northeast Ohio. The selected solution from the implemented approach showed that the area under the curve from the receiver operating characteristic curves had a high discrimination power in separating the areas with high susceptibility. The presented model yielded an accuracy of 85.0 % classifying a total of 13.4 % as high susceptibility area with an overall quantitative index of accuracy corresponding to 0.9082. Based on obtained results, the potential of the presented GP approach for mapping landslide susceptibility is promising and further exploration of its capabilities is suggested for finding new avenues of possible landslide research and practical implementations.
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Data availability
All datasets are derived from LiDAR-based DEMs.
Code availability
Not applicable, “Free and open-source software” (FOSS) software used and referenced in the manuscript.
References
Affenzeller M, Wagner S, Winkler S, Beham A (2009) Genetic algorithms and genetic programming: modern concepts and practical applications. CRC Press, Boca Raton
Atkinson PM, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the central apennines. Italy Geomorphol 130:55–64. https://doi.org/10.1016/j.geomorph.2011.02.001
Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81. https://doi.org/10.1007/s10346-003-0006-9
Bai S-B, Wang J, Lü G-N et al (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31. https://doi.org/10.1016/j.geomorph.2009.09.025
Bathurst JC, Bovolo CI, Cisneros F (2010) Modelling the effect of forest cover on shallow landslides at the river basin scale. Ecol Eng 36:317–327. https://doi.org/10.1016/j.ecoleng.2009.05.001
Blickle T, Thiele L (1996) A comparison of selection schemes used in evolutionary algorithms. Evol Comput 4:361–394. https://doi.org/10.1162/evco.1996.4.4.361
Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436. https://doi.org/10.1007/s10346-014-0550-5
Buffat R, Froemelt A, Heeren N et al (2017) Big data GIS analysis for novel approaches in building stock modelling. Appl Energy 208:277–290. https://doi.org/10.1016/j.apenergy.2017.10.041
Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) Gis technology in mapping landslide hazard. Geographical information systems in assessing natural hazards. Springer, Dordrecht, pp 135–175
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
Chawla S, Shekhar S, Wu W, Ozesmi U (2001) Modeling Spatial Dependencies for Mining Geospatial Data. In: Proceedings of the 2001 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp 1–17
Chipperfield A, Fleming P, Pohlheim H (1994a) A genetic algorithm toolbox for MATLAB. Proc Int Conf Syst Eng 200–207
Chung C-JF, Fabbri AG, Westen CJV (1995) Multivariate regression analysis for landslide hazard zonation. Geographical information systems in assessing natural hazards. Springer, Dordrecht, pp 107–133
Dai FC, Lee CF (2003) A spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regression. Earth Surf Process Landf 28:527–545. https://doi.org/10.1002/esp.456
Dai FC, Lee C-F (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228.
Deng C, Pan H, Fang S et al (2017) Support vector machine as an alternative method for lithology classification of crystalline rocks. J Geophys Eng 14:341–349. https://doi.org/10.1088/1742-2140/aa5b5b
Ercanoglu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64:949–964. https://doi.org/10.1007/s12665-011-0912-4
Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65:2105–2128. https://doi.org/10.1007/s11069-012-0463-3
Gokceoglu C, Sezer E (2009) A statistical assessment on international landslide literature (1945–2008). Landslides 6:345–351. https://doi.org/10.1007/s10346-009-0166-3
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
Gorsevski P (2002) Landslide Hazard Modeling Using GIS. Ph.D. dissertation. University of Idaho, Moscow
Gorsevski P, Gessler P, Jankowski P (2010) A Fuzzy k Means Classification and a Bayesian Approach for Spatial Prediction of Landslide Hazard. https://doi.org/10.1007/978-3-642-03647-7_31
Gorsevski P, Gessler PE, Foltz RB (2000) Spatial prediction of landslides hazard using logistic regression and GIS. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4), Problems, Prospects and Research Needs, Banff, Alberta, Canada. September 2–8
Gorsevski PV (2013) Using Bayesian inference to account for uncertainty in parameter estimates in modelled invasive flowering rush. Remote Sens Lett 4:279–287. https://doi.org/10.1080/2150704X.2012.724539
Gorsevski PV, Brown MK, Panter K et al (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides 13:467–484. https://doi.org/10.1007/s10346-015-0587-0
Gorsevski PV, Gessler PE, Boll J et al (2006a) Spatially and temporally distributed modeling of landslide susceptibility. Geomorphology 80:178–198. https://doi.org/10.1016/j.geomorph.2006.02.011
Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006b) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10:395–415. https://doi.org/10.1111/j.1467-9671.2006.01004.x
Gorsevski PV, Gessler PE, Jankowski P (2003) Integrating a fuzzy k -means classification and a Bayesian approach for spatial prediction of landslide hazard. J Geogr Syst 5:223–251. https://doi.org/10.1007/s10109-003-0113-0
Gorsevski PV, Jankowski P (2008) Discerning landslide susceptibility using rough sets. Comput Environ Urban Syst 32:53–65. https://doi.org/10.1016/j.compenvurbsys.2007.04.001
Gorsevski PV, Jankowski P (2010) An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Comput Geosci 36:1005–1020. https://doi.org/10.1016/j.cageo.2010.03.001
Gorsevski PV, Jankowski P, Gessler PE (2005) Spatial Prediction of Landslide Hazard Using Fuzzy k-means and Dempster-Shafer Theory. Trans GIS 9:455–474. https://doi.org/10.1111/j.1467-9671.2005.00229.x
Gorsevski PV, Jankowski P, Gessler PE (2006c) An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control Cybern 35:121–146
Gotshall S, Rylander B (2002) Optimal Population Size and the Genetic Algorithm. In: WSEAS 2002. Interlaken, Switzerland, p 5
GRASS (2018) Geographic Resources Analysis Support System (GRASS) GIS. https://grass.osgeo.org. Accessed 4 Jun 2018
Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment. Himalayas Eng Geol 28:119–131. https://doi.org/10.1016/0013-7952(90)90037-2
Hamblin S (2012) On the practical usage of genetic algorithms in ecology and evolution. Methods Ecol Evol 11:598
Hansen M (1995) Landslides in Ohio. https://www.dnr.state.oh.us/Portals/10/pdf/GeoFacts/geof08.pdf. Accessed 29 May 2018
Hengl T, Reuter HI (2009) Geomorphometry: concepts, software, applications. development in soil science 33. Elsevier, Amsterdam, p 772. https://www.sciencedirect.com/bookseries/developments-in-soil-science
HeuristicLab (2018) Heuristic and Evolutionary Algorithms Laboratory (HEAL). https://dev.heuristiclab.com/trac.fcgi/. Accessed 1 Jun 2018
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Michigan
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge
Jayawardhana UK, Gorsevski PV (2019) An ontology-based framework for extracting spatio-temporal influenza data using Twitter. Int J Digit Earth 12:2–24. https://doi.org/10.1080/17538947.2017.1411535
Kavzoglu T, Kutlug Sahin E, Colkesen I (2015) Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng Geol 192:101–112. https://doi.org/10.1016/j.enggeo.2015.04.004
Kommenda M, Kronberger G, Wagner S et al (2012) On the architecture and implementation of tree-based genetic programming in HeuristicLab. ACM Press, New York
Korup O, Stolle A (2014) Landslide prediction from machine learning. Geol Today 30:26–33. https://doi.org/10.1111/gto.12034
Koza JR (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection, 1 edition. A Bradford Book, Cambridge, Mass
Krušić J, Marjanović M, Samardžić-Petrović M et al (2017) Comparison of expert, deterministic and Machine Learning approach for landslide susceptibility assessment in Ljubovija Municipality, Serbia. Geofizika 34:251–273. https://doi.org/10.15233/gfz.2017.34.15
Lozano M, Herrera F, Cano J (2008) Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Inf Sci 178:4421–4433. https://doi.org/10.1016/j.ins.2008.07.031
Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234. https://doi.org/10.1016/j.enggeo.2011.09.006
Micheletti N, Foresti L, Robert S et al (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46:33–57. https://doi.org/10.1007/s11004-013-9511-0
Miller BL, Goldberg DE (1995) Genetic Algorithms, Tournament Selection, and the Effects of Noise. 20
Mitchell TM (1997) Machine learning. McGraw-Hill, New York
Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20. https://doi.org/10.1016/j.enggeo.2009.10.001
OGRIP (2018) Ohio Geographically Referenced Information Program. http://ogrip.oit.ohio.gov/. Accessed 31 May 2018
Park N-W (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62:367–376. https://doi.org/10.1007/s12665-010-0531-5
Poli R (2001) Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover. 41
Poli R, Langdon WB, McPhee NF, Koza JR (2008) A field guide to genetic programming. Lulu Press, Morrisville
Reichenbach P, Rossi M, Malamud BD et al (2018) A review of statistically-based landslide susceptibility models. Earth-Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Ronco CCD, Benini E (2014) A simplex-crossover-based multi-objective evolutionary algorithm. IAENG transactions on engineering technologies. Springer, Dordrecht, pp 583–598
SAGA (2018) System for Automated Geoscientific Analyses (SAGA) GIS. http://www.saga-gis.org/en/index.html. Accessed 4 Jun 2018
Saporetti CM, da Fonseca LG, Pereira E (2019) A lithology identification approach based on machine learning with evolutionary parameter tuning. IEEE Geosci Remote Sens Lett 16:1819–1823. https://doi.org/10.1109/LGRS.2019.2911473
Saro L, Woo JS, Kwan-Young O, Moung-Jin L (2016) The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of inje, Korea. Open Geosci. https://doi.org/10.1515/geo-2016-0010
Song K-Y, Oh H-J, Choi J et al (2012) Prediction of landslides using ASTER imagery and data mining models. Adv Space Res 49:978–993. https://doi.org/10.1016/j.asr.2011.11.035
Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115:2564–2577. https://doi.org/10.1016/j.rse.2011.05.013
Szabo JP (1987) Wisconsinan stratigraphy of the Cuyahoga Valley in the Erie Basin, northeastern Ohio. Can J Earth Sci 24:279–290. https://doi.org/10.1139/e87-029
Taalab K, Cheng T, Zhang Y (2018) Mapping landslide susceptibility and types using Random Forest. Big Earth Data 2:159–178. https://doi.org/10.1080/20964471.2018.1472392
Tien Bui D, Ho TC, Revhaug I et al (2014) Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. In: Buchroithner M, Prechtel N, Burghardt D et al (eds) Cartography from pole to pole. Springer Berlin Heidelberg, Berlin, pp 303–317
Tien Bui D, Tuan TA, Hoang N-D et al (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14:447–458. https://doi.org/10.1007/s10346-016-0711-9
Tien Bui D, Tuan TA, Klempe H et al (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
Tong X, Zhang X, Liu M (2010) Detection of urban sprawl using a genetic algorithm-evolved artificial neural network classification in remote sensing: a case study in Jiading and Putuo districts of Shanghai, China. Int J Remote Sens 31:1485–1504. https://doi.org/10.1080/01431160903475290
Tsai F, Lai J-S, Chen WW, Lin T-H (2013) Analysis of topographic and vegetative factors with data mining for landslide verification. Ecol Eng 61:669–677. https://doi.org/10.1016/j.ecoleng.2013.07.070
Wagner S, Kronberger G, Beham A et al (2014) Architecture and design of the HeuristicLab optimization environment. In: Klempous R, Nikodem J, Jacak W, Chaczko Z et al (eds) Advanced methods and applications in computational intelligence. Springer International Publishing, Heidelberg, pp 197–261
Westen CJ van, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86:404–414. https://doi.org/10.1007/s005310050149
Wilson J, Gallant J (2000) Terrain Analysis: Principles and Applications
Winkler S, Affenzeller M, Wagner S (2007) Advanced genetic programming based machine learning. J Math Model Algorithms 6:455–480. https://doi.org/10.1007/s10852-007-9065-6
Winkler SM, Affenzeller M, Wagner S (2009) Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis. Genet Program Evolvable Mach 10:111–140. https://doi.org/10.1007/s10710-008-9076-8
Yang M-D (2007) A genetic algorithm (GA) based automated classifier for remote sensing imagery. Can J Remote Sens 33:203–213. https://doi.org/10.5589/m07-020
Yang M-D, Yang Y-F, Su T-C, Huang K-S (2014) An efficient fitness function in genetic algorithm classifier for Landuse recognition on satellite images. Sci World J. https://doi.org/10.1155/2014/264512
Yao H, Hamilton HJ (2008) Mining functional dependencies from data. Data Min Knowl Discov 16:197–219. https://doi.org/10.1007/s10618-007-0083-9
Zêzere JL, Pereira S, Melo R et al (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267. https://doi.org/10.1016/j.scitotenv.2017.02.188
Zojaji Z, Ebadzadeh MM (2016) Semantic schema theory for genetic programming. Appl Intell 44:67–87. https://doi.org/10.1007/s10489-015-0696-4
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Gorsevski, P.V. An evolutionary approach for spatial prediction of landslide susceptibility using LiDAR and symbolic classification with genetic programming. Nat Hazards 108, 2283–2307 (2021). https://doi.org/10.1007/s11069-021-04780-z
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DOI: https://doi.org/10.1007/s11069-021-04780-z