Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods

Ahmadlou, 2018, Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA), Geocarto Int., 1 Bhowmick, 2010, Application of alternating decision trees in selecting sparse linear solvers, 153 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Bui, 2018, Novel hybrid evolutionary algorithms for spatial prediction of floods, Sci. Rep., 8, 15364, 10.1038/s41598-018-33755-7 Chapi, 2017, A novel hybrid artificial intelligence approach for flood susceptibility assessment, Environ. Modell. Software, 95, 229, 10.1016/j.envsoft.2017.06.012 Chen, 2015, Flood hazard assessment in the Kujukuri Plain of Chiba Prefecture, Japan, based on GIS and multicriteria decision analysis, Nat. Hazards, 78, 105, 10.1007/s11069-015-1699-5 Chen, 2019, Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles, J. Hydrol., 575, 864, 10.1016/j.jhydrol.2019.05.089 Chen, 2019, Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO), Appl. Sci., 9, 3755, 10.3390/app9183755 Chen, 2018, GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models, Sci. Total Environ., 634, 853, 10.1016/j.scitotenv.2018.04.055 Chen, 2019, Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization, J. Hydrol., 572, 435, 10.1016/j.jhydrol.2019.03.013 Chen, 2018, Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms, Bull. Eng. Geol. Environ., 77, 611, 10.1007/s10064-017-1004-9 Chen, 2019, Novel hybrid integration approach of bagging-based Fisher’s linear discriminant function for groundwater potential analysis, Nat. Resour. Res., 28, 1239, 10.1007/s11053-019-09465-w Chen, 2018, Landslide susceptibility modeling based on GIS and novel bagging-based kernel logistic regression, Appl. Sci., 8, 2540, 10.3390/app8122540 Chen, 2017, 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, Geomat., Nat. Hazards Risk, 8, 1955, 10.1080/19475705.2017.1401560 Chen, 2019, Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models, Appl. Sci., 9, 171, 10.3390/app9010171 Chen, 2019, Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods, Sci. Total Environ., 684, 31, 10.1016/j.scitotenv.2019.05.312 Chen, 2018, GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method, Catena, 164, 135, 10.1016/j.catena.2018.01.012 Chen, 2019, Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree, Geocarto Int., 34, 1177, 10.1080/10106049.2019.1588393 Choubin, 2019, An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines, Sci. Total Environ., 651, 2087, 10.1016/j.scitotenv.2018.10.064 Dahri, 2017, Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia), Environ. Earth Sci., 76, 302, 10.1007/s12665-017-6619-4 de Santana, 2018, Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters, Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 191, 454, 10.1016/j.saa.2017.10.052 Dou, 2014, GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu Area, Central Japan, 419 Du, 2013, Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China, Stoch. Env. Res. Risk Assess., 27, 377, 10.1007/s00477-012-0589-6 Dutta, D., Herath, S., 2004. Trend of floods in Asia and flood risk management with integrated river basin approach. In: Proceeding of 2nd Asian Pacific association of hydrology and water resources and conference, pp. 55–63. Farid, 2014, Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks, Expert Syst. Appl., 41, 1937, 10.1016/j.eswa.2013.08.089 Farnaaz, 2016, Random forest modeling for network intrusion detection system, Procedia Comput. Sci., 89, 213, 10.1016/j.procs.2016.06.047 Freund, Y., Mason, L., 1999. The alternating decision tree learning algorithm. In: Proceedings of the Sixteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., pp. 124–133. Guo, C., Qin, Y., Ma, D., Xia, Y., Chen, Y., Si, Q., Lu, L., 2019. Ionic composition, geological signature and environmental impacts of coalbed methane produced water in China. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1–15. Han, 2011 Heckerman, D., 1986. Probabilistic interpretations for Mycin's certainty factors. 4, pp. 167–196. Hong, 2018, Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China), CATENA, 163, 399, 10.1016/j.catena.2018.01.005 Hong, 2019, Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping, CATENA, 176, 45, 10.1016/j.catena.2018.12.035 Hong, 2015, Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines, CATENA, 133, 266, 10.1016/j.catena.2015.05.019 Hong, 2018, Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China, Sci. Total Environ., 625, 575, 10.1016/j.scitotenv.2017.12.256 Hosseinalizadeh, 2019, Gully headcut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models, Geoderma, 342, 1, 10.1016/j.geoderma.2019.01.050 Ilia, 2015, A Geographical Information System (GIS) Based Probabilistic Certainty Factor Approach in Assessing Landslide Susceptibility: The Case Study of Kimi, Euboea, Greece, Vol. 2, 1199 Khosravi, 2016, A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique, Nat. Hazards, 83, 947, 10.1007/s11069-016-2357-2 Khosravi, 2018, A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran, Sci. Total Environ., 627, 744, 10.1016/j.scitotenv.2018.01.266 Khosravi, 2019, A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods, J. Hydrol., 573, 311, 10.1016/j.jhydrol.2019.03.073 Kia, 2012, An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia, Environ. Earth Sci., 67, 251, 10.1007/s12665-011-1504-z Liu, 2015, Susceptibility of Rocky Desertification Based on Analytical Hierarchy Process and Certainty Factor (AHP-CF) Method: A Case from Changshun County, Guizhou Province, SW of China, Vol. 3, 511 Liu, 2005, Boosting alternating decision trees modeling of disease trait information, BMC Genet., 6, S132, 10.1186/1471-2156-6-S1-S132 Liu, 2016, Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia, Stoch. Env. Res. Risk Assess., 30, 1575, 10.1007/s00477-015-1198-y Ministry of Water Resources of China (MWRC), 2014. Bulletin of Flood and Drought Disaster in China (in Chinese). Pfahringer, B., Holmes, G., Kirkby, R., 2001. Optimizing the induction of alternating decision trees, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, April 16, 2001 - April 18, 2001. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, Kowloon, Hong kong, pp. 477–487. Pham, 2016, A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area, Geotech. Geol. Eng., 34, 1807, 10.1007/s10706-016-9990-0 Pham, 2017, Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study, Geotech. Geol. Eng., 35, 2597, 10.1007/s10706-017-0264-2 Pourghasemi, 2013, Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran, Arab. J. Geosci., 6, 2351, 10.1007/s12517-012-0532-7 Quiroz, 2018, Fault detection of broken rotor bar in LS-PMSM using random forests, Measurement, 116, 273, 10.1016/j.measurement.2017.11.004 Rahmati, 2017, Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models, Water Resour. Manage., 31, 1473, 10.1007/s11269-017-1589-6 Rahmati, 2016, Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran, Geocarto Int., 31, 42, 10.1080/10106049.2015.1041559 Razavi Termeh, 2018, Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms, Sci. Total Environ., 615, 438, 10.1016/j.scitotenv.2017.09.262 Renyi, 2002, Flood area and damage estimation in Zhejiang, China, J. Environ. Manage., 66, 1, 10.1006/jema.2002.0544 Shafizadeh-Moghadam, 2018, Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping, J. Environ. Manage., 217, 1, 10.1016/j.jenvman.2018.03.089 Shortliffe, 1975, A model of inexact reasoning in medicine, Math. Biosci., 23, 351, 10.1016/0025-5564(75)90047-4 Shu, 2008, Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system, J. Hydrol., 349, 31, 10.1016/j.jhydrol.2007.10.050 Smith, 2013 Smith, 1998 Sok, 2016, Multivariate alternating decision trees, Pattern Recogn., 50, 195, 10.1016/j.patcog.2015.08.014 Sujatha, 2012, Landslide susceptibility analysis using Probabilistic Certainty Factor Approach: A case study on Tevankarai stream watershed, India, J. Earth Syst. Sci., 121, 1337, 10.1007/s12040-012-0230-6 Tehrany, 2013, Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS, J. Hydrol., 504, 69, 10.1016/j.jhydrol.2013.09.034 Tehrany, 2015, Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method, Stoch. Env. Res. Risk Assess., 29, 1149, 10.1007/s00477-015-1021-9 Tehrany, 2015, Flood susceptibility assessment using GIS-based support vector machine model with different kernel types, Catena, 125, 91, 10.1016/j.catena.2014.10.017 Tien Bui, 2018, New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling, Water, 10, 1210, 10.3390/w10091210 Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F.M., Melesse, A., Thai Pham, B., Pourghasemi, H.R., Mahmoudi, M., Bahrami, S., 2019a. Flood spatial modeling in northern Iran using remote sensing and gis: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sens. 11(13), 1589. Tien Bui, 2016, Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS, J. Hydrol., 540, 317, 10.1016/j.jhydrol.2016.06.027 Tien Bui, 2019, Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods, Sci. Total Environ., 668, 1038, 10.1016/j.scitotenv.2019.02.422 Tsagkrasoulis, 2018, Random forest regression for manifold-valued responses, Pattern Recogn. Lett., 101, 6, 10.1016/j.patrec.2017.11.008 Van Boeckel, 2012, Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemic, PLoS ONE, 7, 10.1371/journal.pone.0049528 Vandenberg-Rodes, 2016, Projecting nuisance flooding in a warming climate using generalized linear models and Gaussian processes, J. Geophys. Res. Oceans, 121, 8008, 10.1002/2016JC012084 Wang, 1992, Improvements on certainty factor model and its application to conflict resolution in CDPS, J. Comput. Sci. Technol., 7, 143, 10.1007/BF02945768 Wang, 2006, Combining decision tree and Naive Bayes for classification, Knowl.-Based Syst., 19, 511, 10.1016/j.knosys.2005.10.013 Wang, 2015, Adapting naive Bayes tree for text classification, Knowl. Inf. Syst., 44, 77, 10.1007/s10115-014-0746-y Wang, 2019, Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm, J. Environ. Manage., 247, 712, 10.1016/j.jenvman.2019.06.102 Witten, 2017, Data Mining: Practical Machine Learning Tools and Techniques, 161 Witten, 2011 Wu, 2018, A generalized tree augmented naive Bayes link prediction model, J. Comput. Sci., 27, 206, 10.1016/j.jocs.2018.04.006 Youssef, 2016, Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models, Environ. Earth Sci., 75, 12, 10.1007/s12665-015-4830-8 Yu, 2009, Analysis of historical floods on the Yangtze River, China: Characteristics and explanations, Geomorphology, 113, 210, 10.1016/j.geomorph.2009.03.008 Zhang, 2009, Search for the smallest random forest, Stat. Interface, 2 Zhao, 2019, Assessment of urban flood susceptibility using semi-supervised machine learning model, Sci. Total Environ., 659, 940, 10.1016/j.scitotenv.2018.12.217 Zhao, 2018, Mapping flood susceptibility in mountainous areas on a national scale in China, Sci. Total Environ., 615, 1133, 10.1016/j.scitotenv.2017.10.037 Zheng, 1996, Application of certainty factors of earthquake precursory anomaly evidences CF(E), Acta Seismol. Sin., 9, 163, 10.1007/BF02650635 Zhou, 2018, Random forest for label ranking, Expert Syst. Appl., 112, 99, 10.1016/j.eswa.2018.06.036