A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)
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Biswajeet Pradhan | Saro Lee | Dieu Tien Bui | Himan Shahabi | Ataollah Shirzadi | Kamran Chapi | Binh Thai Pham | Baharin Bin Ahmad | Gyula Gróf | Hossein Khaledian | Mahdi Panahi | Hosein Rahmani | Ebrahim Omidavr | Dawood Talebpour Asl | B. Pradhan | D. Bui | B. Pham | M. Panahi | Saro Lee | Gyula Gróf | H. Shahabi | B. Ahmad | A. Shirzadi | K. Chapi | Ebrahim Omidavr | H. Khaledian | H. Rahmani
[1] J. Brice. Erosion and deposition in the loess-mantled Great Plains, Medicine Creek drainage basin, Nebraska , 1966 .
[2] D. Hosmer,et al. Goodness of fit tests for the multiple logistic regression model , 1980 .
[3] J. Poesen,et al. Gully erosion in the loam belt of Belgium: typology and control measures. , 1990 .
[4] I. Moore,et al. Length-slope factors for the Revised Universal Soil Loss Equation: simplified method of estimation , 1992 .
[5] J. Poesen. Gully typology and gully control measures in the European loess belt , 1993 .
[6] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[7] J V Tu,et al. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.
[8] J. Poesen,et al. Contribution of gully erosion to sediment production in cultivated lands and rangelands , 1996 .
[9] L. H. Cammeraat,et al. The effect of land use on runoff and soil erosion rates under Mediterranean conditions , 1997 .
[10] A. Hjelmfelt,et al. Hydrologic Soil Group Assignment , 1998 .
[11] Yoav Freund,et al. The Alternating Decision Tree Learning Algorithm , 1999, ICML.
[12] R. Bryan. Soil erodibility and processes of water erosion on hillslope , 2000 .
[13] J. Poesen,et al. Spatial distribution of gully head activity and sediment supply along an ephemeral channel in a Mediterranean environment , 2000 .
[14] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[15] H. White,et al. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. , 2001, Journal of clinical epidemiology.
[16] J. Poesen,et al. Gully erosion in dryland environments , 2002 .
[17] M. Kirkby,et al. Dryland Rivers: Hydrology and Geomorphology of Semi-arid Channels , 2002 .
[18] V. Souchère,et al. Rill erosion on cultivated hillslopes during two extreme rainfall events in Normandy, France , 2002 .
[19] Rémi Gilleron,et al. Learning Multi-label Alternating Decision Trees from Texts and Data , 2003, MLDM.
[20] J. Poesen,et al. Gully erosion and environmental change: importance and research needs , 2003 .
[21] R. Lal,et al. Offsetting global CO2 emissions by restoration of degraded soils and intensification of world agriculture and forestry , 2003 .
[22] F. Dramis,et al. Geomorphological investigation on gully erosion in the Rift Valley and the northern highlands of Ethiopia , 2003 .
[23] Aníbal Pauchard,et al. Influence of Elevation, Land Use, and Landscape Context on Patterns of Alien Plant Invasions along Roadsides in Protected Areas of South‐Central Chile , 2004 .
[24] C. Valentin,et al. Spatial and temporal assessment of linear erosion in catchments under sloping lands of northern Laos , 2005 .
[25] Mahesh Pal,et al. Random forest classifier for remote sensing classification , 2005 .
[26] J. Poesen,et al. Gully erosion: Impacts, factors and control , 2005 .
[27] L. H. Cammeraat,et al. Identification of vulnerable areas for gully erosion under different scenarios of land abandonment in Southeast Spain , 2007 .
[28] Hae-Chang Rim,et al. Some Effective Techniques for Naive Bayes Text Classification , 2006, IEEE Transactions on Knowledge and Data Engineering.
[29] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] T. Fearn,et al. Classification and Regression Trees (CART) , 2020, Statistical Learning from a Regression Perspective.
[31] Yumei Li,et al. Facies identification from well logs: A comparison of discriminant analysis and naïve Bayes classifier , 2006 .
[32] P. Cortez,et al. A data mining approach to predict forest fires using meteorological data , 2007 .
[33] John P. Wilson,et al. Use of terrain variables for mapping gully erosion susceptibility in Lebanon , 2007 .
[34] M. Eeckhaut,et al. Spatial analysis of factors controlling the presence of closed depressions and gullies under forest: Application of rare event logistic regression , 2008 .
[35] Shalabh. Statistical Learning from a Regression Perspective , 2009 .
[36] Taskin Kavzoglu,et al. A kernel functions analysis for support vector machines for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.
[37] P. Kuhnert,et al. Incorporating uncertainty in gully erosion calculations using the random forests modelling approach , 2009 .
[38] S. Padmavathi. Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils , 2009 .
[39] Houkuan Huang,et al. Feature selection for text classification with Naïve Bayes , 2009, Expert Syst. Appl..
[40] G. H. Holliday,et al. Glossary of Soil Science Terms , 1965, Soil Science Society of America Journal.
[41] C. Woltemade. Impact of Residential Soil Disturbance on Infiltration Rate and Stormwater Runoff 1 , 2010 .
[42] B. Pradhan. Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing , 2010 .
[43] M. Tech,et al. Decision Support in Heart Disease Prediction System using Naive Bayes , 2011 .
[44] Li Wang,et al. Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Pla , 2011 .
[45] B. Schröder,et al. A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy , 2011 .
[46] E. Rotigliano,et al. Multi parametric GIS analysis to assess gully erosion susceptibility : a test in southern Sicily, Italy , 2011 .
[47] M. Conforti,et al. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy) , 2011 .
[48] L. Gawrysiak,et al. Spatial diversity of gully density of the Lublin Upland and Roztocze Hills (SE Poland) , 2012 .
[49] Akin Ozcift,et al. SVM Feature Selection Based Rotation Forest Ensemble Classifiers to Improve Computer-Aided Diagnosis of Parkinson Disease , 2012, Journal of medical systems.
[50] M. R. Boussema,et al. Sediment yield from irregularly shaped gullies located on the Fortuna lithologic formation in semi-arid area of Tunisia , 2012 .
[51] T. Svoray,et al. Predicting gully initiation: comparing data mining techniques, analytical hierarchy processes and the topographic threshold , 2012 .
[52] Cristiano Ballabio,et al. Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy , 2012, Mathematical Geosciences.
[53] A. Danladi,et al. An analysis of some soil properties along gully erosion sites under different land use areas of Gombe Metropolis, Gombe State, Nigeria , 2014 .
[54] E. Rotigliano,et al. Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy) , 2014 .
[55] Mustafa Neamah Jebur,et al. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia , 2014 .
[56] Hong-chun Zhu,et al. Extraction and analysis of gully head of Loess Plateau in China based on digital elevation model , 2014, Chinese Geographical Science.
[57] M. Maerker,et al. Prediction of gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: A case study in the Mazayejan plain, southwest Iran , 2014 .
[58] Mustafa Neamah Jebur,et al. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .
[59] Mustafa Neamah Jebur,et al. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale , 2014 .
[60] A. Murwira,et al. Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District – Zimbabwe , 2014 .
[61] G. Janicki,et al. Development of bank gullies on the shore zone of the Bratsk Reservoir (Russia) , 2014 .
[62] B. Pradhan,et al. A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam , 2014 .
[63] Peijun Du,et al. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features , 2015 .
[64] W. Kociuba,et al. Comparison of volumetric and remote sensing methods (TLS) for assessing the development of a permanent forested loess gully , 2015, Natural Hazards.
[65] 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.
[66] E. Rotigliano,et al. Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations , 2015, Natural Hazards.
[67] Sutee Anantsuksomsri,et al. Alternative Strategies for Mapping ACS Estimates and Error of Estimation , 2015 .
[68] J. Poesen,et al. How fast do gully headcuts retreat , 2016 .
[69] Mahyat Shafapour Tehrany,et al. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types , 2015 .
[70] Majid Shadman Roodposhti,et al. Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method , 2016, Entropy.
[71] Dieu Tien Bui,et al. Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression , 2016, Remote. Sens..
[72] H. Pourghasemi,et al. Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison , 2016, Natural Hazards.
[73] T. Steenhuis,et al. A Biophysical and Economic Assessment of a Community‐based Rehabilitated Gully in the Ethiopian Highlands , 2016 .
[74] B. Pradhan,et al. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks , 2016, Environmental Earth Sciences.
[75] B. Pham,et al. Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS , 2016, Natural Hazards.
[76] Dieu Tien Bui,et al. A novel hybrid artificial intelligence approach for flood susceptibility assessment , 2017, Environ. Model. Softw..
[77] D. Bui,et al. A Novel Hybrid Model of Rotation Forest Based Functional Trees for Landslide Susceptibility Mapping: A Case Study at Kon Tum Province, Vietnam , 2017 .
[78] D. Bui,et al. A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS , 2017 .
[79] D. Bui,et al. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach , 2017, Environmental Earth Sciences.
[80] H. Pourghasemi,et al. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. , 2017, The Science of the total environment.
[81] H. Shahabi,et al. Drought sensitivity mapping using two one-class support vector machine algorithms , 2017 .
[82] Wei Chen,et al. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China , 2017 .
[83] H. Pourghasemi,et al. Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. , 2017, The Science of the total environment.
[84] Wei Chen,et al. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models , 2017 .
[85] D. Bui,et al. A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides Using GIS , 2017 .
[86] A. Zhu,et al. A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China) , 2017, Environmental Earth Sciences.
[87] V. Singh,et al. New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling , 2018, Water.
[88] A-Xing Zhu,et al. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. , 2018, The Science of the total environment.
[89] S. Pulley,et al. Gully erosion as a mechanism for wetland formation: An examination of two contrasting landscapes , 2018 .
[90] Wei Chen,et al. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. , 2018, The Science of the total environment.
[91] H. Pourghasemi,et al. GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique , 2018, Environmental Earth Sciences.
[92] H. Shahabi,et al. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. , 2018, Journal of environmental management.
[93] Wei Chen,et al. Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms , 2018, Sensors.
[94] Wei Chen,et al. Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling , 2018, Entropy.
[95] Himan Shahabi,et al. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment , 2018, Geocarto International.
[96] B. Pham,et al. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. , 2018, The Science of the total environment.
[97] Dieu Tien Bui,et al. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling , 2019, Bulletin of Engineering Geology and the Environment.
[98] V. Singh,et al. Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach , 2018, Water Resources Management.
[99] Wei Chen,et al. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment , 2018 .
[100] Biswajeet Pradhan,et al. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping , 2018, Sensors.
[101] A. Al-Abadi,et al. Susceptibility mapping of gully erosion using GIS-based statistical bivariate models: a case study from Ali Al-Gharbi District, Maysan Governorate, southern Iraq , 2018, Environmental Earth Sciences.
[102] Biswajeet Pradhan,et al. Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches , 2018, Journal of Hydrology.
[103] Xiaojing Wang,et al. Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression , 2018, Applied Sciences.
[104] B. Pradhan,et al. Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm , 2018, Land Degradation & Development.
[105] V. Singh,et al. Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods , 2018, Scientific Reports.
[106] Wei Chen,et al. Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia , 2018, Remote. Sens..
[107] A. Zhu,et al. Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling , 2018, Bulletin of Engineering Geology and the Environment.
[108] Xing Chen,et al. DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model , 2018, Neurocomputing.
[109] 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.
[110] Tri Dev Acharya,et al. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) , 2018 .
[111] A-Xing Zhu,et al. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. , 2018, The Science of the total environment.
[112] Nhat-Duc Hoang,et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides , 2018, Remote. Sens..
[113] Wei Chen,et al. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility , 2019, CATENA.
[114] Dieu Tien Bui,et al. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility , 2019, CATENA.
[115] D. Bui,et al. Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches , 2019, CATENA.
[116] Himan Shahabi,et al. Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA) , 2019 .
[117] Omid Ghorbanzadeh,et al. A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia , 2019, Sensors.
[118] B. Pradhan,et al. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. , 2019, Journal of environmental management.
[119] D. Bui,et al. Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling , 2019, Forests.
[120] D. Bui,et al. Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution , 2019, CATENA.
[121] Francisco Gutiérrez,et al. Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms , 2019, Land Degradation & Development.
[122] B. Pham,et al. Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping , 2019 .
[123] Himan Shahabi,et al. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. , 2019, The Science of the total environment.
[124] Biswajeet Pradhan,et al. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm , 2019, Remote. Sens..
[125] Himan Shahabi,et al. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability , 2019, Agricultural and Forest Meteorology.
[126] Saro Lee,et al. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. , 2019, The Science of the total environment.
[127] Nadhir Al-Ansari,et al. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms , 2020, International journal of environmental research and public health.