A Novel Performance Assessment Approach Using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest

Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides.

[1]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[2]  Wei Chen,et al.  Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods , 2018, Natural Hazards.

[3]  Kazuhide Sawada,et al.  Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units , 2018, Geosciences Journal.

[4]  Chong Xu,et al.  Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event , 2018, Geomatics, Natural Hazards and Risk.

[5]  B. Pradhan,et al.  A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility , 2017 .

[6]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[7]  Paraskevas Tsangaratos,et al.  Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size , 2016 .

[8]  Candan Gokceoglu,et al.  The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity , 2005 .

[9]  J. N. Hutchinson,et al.  Suggested nomenclature for landslides , 1990 .

[10]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[11]  Wei Chen,et al.  Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China) , 2019, Bulletin of Engineering Geology and the Environment.

[12]  Candan Gokceoglu,et al.  A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality , 2019, ISPRS Int. J. Geo Inf..

[13]  Zohre Sadat Pourtaghi,et al.  Landslide susceptibility assessment in Lianhua County (China); a comparison between a random forest data mining technique and bivariate and multivariate statistical models , 2016 .

[14]  John P. Wilson,et al.  Terrain analysis : principles and applications , 2000 .

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Troy Shinbrot,et al.  Topography of inland deltas: Observations, modeling, and experiments , 2010 .

[17]  Biswajeet Pradhan,et al.  An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm , 2012, Comput. Geosci..

[18]  Candan Gokceoglu,et al.  A CitSci app for landslide data collection , 2018, Landslides.

[19]  Joong-Sun Won,et al.  Spatial Landslide Hazard Prediction Using Rainfall Probability and a Logistic Regression Model , 2015, Mathematical Geosciences.

[20]  D. Montgomery,et al.  Landslide erosion controlled by hillslope material , 2010 .

[21]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[22]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[23]  A. Grün,et al.  LEAST SQUARES 3D SURFACE MATCHING , 2004 .

[24]  Candan Gokceoglu,et al.  An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[25]  H. A. Nefeslioglu,et al.  An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps , 2008 .

[26]  A. Gruen,et al.  Least squares 3D surface and curve matching , 2005 .

[27]  P. Reichenbach,et al.  A review of statistically-based landslide susceptibility models , 2018 .

[28]  B. C. Ozer,et al.  On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco) , 2019, Bulletin of Engineering Geology and the Environment.

[29]  Giovanni B. Crosta,et al.  Engineering geology - A fifty year perspective , 2016 .

[30]  C. Gokceoğlu,et al.  A statistical assessment on international landslide literature (1945–2008) , 2009 .

[31]  Wei Chen,et al.  A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping , 2017 .

[32]  Christos Polykretis,et al.  A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece) , 2015, Bulletin of Engineering Geology and the Environment.

[33]  H. A. Nefeslioglu,et al.  Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses , 2005 .

[34]  H. Rahali,et al.  Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco , 2019 .

[35]  Biswajeet Pradhan,et al.  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 , 2016, Landslides.

[36]  A. Zhu,et al.  Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping , 2019, CATENA.

[37]  Devrim Akca,et al.  Co-registration of surfaces by 3D least squares matching. , 2010 .

[38]  H. A. Nefeslioglu,et al.  An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software , 2017, Comput. Geosci..

[39]  Zohre Sadat Pourtaghi,et al.  Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia , 2015, Landslides.

[40]  E. Baltsavias,et al.  TESTS AND PERFORMANCE EVALUATION OF DMC IMAGES AND NEW METHODS FOR THEIR PROCESSING , 2006 .

[41]  B. Pham,et al.  Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. , 2019, The Science of the total environment.

[42]  H. A. Nefeslioglu,et al.  Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey) , 2008 .

[43]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[44]  Matthias Schroder,et al.  Logistic Regression: A Self-Learning Text , 2003 .

[45]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[46]  D. Varnes,et al.  Landslide types and processes , 2004 .

[47]  Ebru Akcapinar Sezer,et al.  GeoFIS: An integrated tool for the assessment of landslide susceptibility , 2014, Comput. Geosci..

[48]  A. Grün,et al.  3D modeling of the Weary Herakles statue with a coded structured light system , 2006 .

[49]  H. A. Nefeslioglu,et al.  Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey , 2006 .

[50]  Devrim Akca,et al.  Monitoring of a Laboratory‐Scale Inland‐Delta Formation using a Structured‐Light System , 2016 .

[51]  D. Bui,et al.  A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India , 2017, International Journal of Sediment Research.

[52]  J. Iqbal,et al.  Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China , 2017, Journal of Mountain Science.