Earth fissure hazard prediction using machine learning models.

[1]  S. Shamshirband,et al.  Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain. , 2019, The Science of the total environment.

[2]  V. Singh,et al.  Snow avalanche hazard prediction using machine learning methods , 2019, Journal of Hydrology.

[3]  Frederic Coulon,et al.  Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods. , 2019, The Science of the total environment.

[4]  Vijay P. Singh,et al.  Applying the remotely sensed data to identify homogeneous regions of watersheds using a pixel-based classification approach , 2019, Applied Geography.

[5]  Jian-bing Peng,et al.  A typical Earth fissure resulting from loess collapse on the loess plateau in the Weihe Basin, China , 2019, Engineering Geology.

[6]  Abdullah Al Mamun,et al.  Modelling of Asphalt's Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis , 2019, Comput. Intell. Neurosci..

[7]  Pijush Samui,et al.  A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area , 2019, Journal of Hydrology.

[8]  Pijush Samui,et al.  A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping , 2019, CATENA.

[9]  Qiang Chen,et al.  Geodetic and hydrological measurements reveal the recent acceleration of groundwater depletion in North China Plain , 2019, Journal of Hydrology.

[10]  Lingxiao Tang,et al.  Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China , 2019, Technological Forecasting and Social Change.

[11]  Deyi Hou,et al.  Groundwater depletion and contamination: Spatial distribution of groundwater resources sustainability in China. , 2019, The Science of the total environment.

[12]  Zhongyun Ni,et al.  Land Subsidence and Ground Fissures in Beijing Capital International Airport (BCIA): Evidence from Quasi-PS InSAR Analysis , 2019, Remote. Sens..

[13]  Wei Chen,et al.  Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree , 2019, Geocarto International.

[14]  H. Pourghasemi,et al.  Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. , 2019, The Science of the total environment.

[15]  Candace Chow,et al.  Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events. , 2019, Journal of environmental management.

[16]  Wen-Chieh Cheng,et al.  A review on land subsidence caused by groundwater withdrawal in Xi’an, China , 2019, Bulletin of Engineering Geology and the Environment.

[17]  Peter Tiefenbacher,et al.  Climate Change, Land Use/Land Cover Change, and Population Growth as Drivers of Groundwater Depletion in the Central Valleys, Oaxaca, Mexico , 2019, Remote. Sens..

[18]  S. Shamshirband,et al.  Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin , 2019, Water.

[19]  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.

[20]  Mingdong Zang,et al.  Earth fissures developed within collapsible loess area caused by groundwater uplift in Weihe watershed, northwestern China , 2019, Journal of Asian Earth Sciences.

[21]  George M. Hornberger,et al.  Identifying El Niño–Southern Oscillation influences on rainfall with classification models: implications for water resource management of Sri Lanka , 2019, Hydrology and Earth System Sciences.

[22]  Haekyung Park,et al.  Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data , 2019, Water.

[23]  Ravinesh C. Deo,et al.  Land subsidence modelling using tree-based machine learning algorithms. , 2019, The Science of the total environment.

[24]  S. Sorooshian,et al.  Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network , 2019, Water.

[25]  A. Othman,et al.  Land subsidence triggered by groundwater withdrawal under hyper-arid conditions: case study from Central Saudi Arabia , 2019, Environmental Earth Sciences.

[26]  Cristián Bravo,et al.  Advanced turbidity prediction for operational water supply planning , 2019, Decis. Support Syst..

[27]  Saro Lee,et al.  Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models , 2019, Applied Sciences.

[28]  Shahaboddin Shamshirband,et al.  Estimating Daily Dew Point Temperature Using Machine Learning Algorithms , 2019, Water.

[29]  Mahdi Doostparast,et al.  One-way classification with random effects: A reversed-hazard-based approach , 2019, J. Comput. Appl. Math..

[30]  H. Michael,et al.  Offshore Pumping Impacts Onshore Groundwater Resources and Land Subsidence , 2019, Geophysical Research Letters.

[31]  P. Njage,et al.  Improving hazard characterization in microbial risk assessment using next generation sequencing data and machine learning: Predicting clinical outcomes in shigatoxigenic Escherichia coli. , 2019, International journal of food microbiology.

[32]  Huiming Tang,et al.  Deformation Monitoring of Earth Fissure Hazards Using Terrestrial Laser Scanning , 2019, Sensors.

[33]  V. Kutala,et al.  Classification and regression tree-based prediction of 6-mercaptopurine-induced leucopenia grades in children with acute lymphoblastic leukemia , 2019, Cancer Chemotherapy and Pharmacology.

[34]  J. Adamowski,et al.  An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. , 2019, The Science of the total environment.

[35]  Dongmei Han,et al.  Deformation of the aquifer system under groundwater level fluctuations and its implication for land subsidence control in the Tianjin coastal region , 2019, Environmental Monitoring and Assessment.

[36]  Omid Rahmati,et al.  Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities. , 2019, Journal of environmental management.

[37]  Wanfang Zhou,et al.  Overview of ground fissure research in China , 2019, Environmental Earth Sciences.

[38]  B. Pradhan,et al.  Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques , 2019, Journal of Hydrology.

[39]  Lifeng Wu,et al.  Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China , 2019, Renewable and Sustainable Energy Reviews.

[40]  Zaher Mundher Yaseen,et al.  An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction , 2019, Journal of Hydrology.

[41]  T. Farr,et al.  Role of agricultural activity on land subsidence in the San Joaquin Valley, California , 2019, Journal of Hydrology.

[42]  Tom Dijkstra,et al.  Loess geohazards research in China: Advances and challenges for mega engineering projects , 2019, Engineering Geology.

[43]  Alexei Lyapustin,et al.  Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model. , 2019, Environment international.

[44]  Jian-bing Peng,et al.  Dynamic characteristics of a ground fissure site , 2019, Engineering Geology.

[45]  Jonathan J Gourley,et al.  Toward Probabilistic Prediction of Flash Flood Human Impacts , 2019, Risk analysis : an official publication of the Society for Risk Analysis.

[46]  B. Pradhan,et al.  A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. , 2018, The Science of the total environment.

[47]  Qiang Xu,et al.  A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides , 2018, Neural Computing and Applications.

[48]  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.

[49]  Jungho Im,et al.  Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia , 2018, Remote. Sens..

[50]  Dongmei Han,et al.  Phase difference between groundwater storage changes and groundwater level fluctuations due to compaction of an aquifer-aquitard system , 2018, Journal of Hydrology.

[51]  M. Hashemi,et al.  Geoenvironmental assessment of the formation and expansion of earth fissures as geological hazards along the route of the Haram-to-Haram Highway, Iran , 2018, Bulletin of Engineering Geology and the Environment.

[52]  Kwok-wing Chau,et al.  Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.

[53]  Paul Muñoz,et al.  Flash-Flood Forecasting in an Andean Mountain Catchment—Development of a Step-Wise Methodology Based on the Random Forest Algorithm , 2018, Water.

[54]  Li Xiang-yang,et al.  Improving emergency response to cascading disasters: Applying case-based reasoning towards urban critical infrastructure , 2018, International Journal of Disaster Risk Reduction.

[55]  Ravinesh C. Deo,et al.  Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting , 2018, Comput. Electron. Agric..

[56]  Guanxing Huang,et al.  Heavy metal(loid)s and organic contaminants in groundwater in the Pearl River Delta that has undergone three decades of urbanization and industrialization: Distributions, sources, and driving forces. , 2018, The Science of the total environment.

[57]  A. Rajabi A numerical study on land subsidence due to extensive overexploitation of groundwater in Aliabad plain, Qom-Iran , 2018, Natural Hazards.

[58]  Keunje Yoo,et al.  Classification and Regression Tree Approach for Prediction of Potential Hazards of Urban Airborne Bacteria during Asian Dust Events , 2018, Scientific Reports.

[59]  C. Da Lio,et al.  Land subsidence in the Friuli Venezia Giulia coastal plain, Italy: 1992-2010 results from SAR-based interferometry. , 2018, The Science of the total environment.

[60]  A. Pouliakis,et al.  Classification and regression trees for the evaluation of thyroid cytomorphological characteristics: A study based on liquid based cytology specimens from thyroid fine needle aspirations , 2018, Diagnostic cytopathology.

[61]  R. Khatibi,et al.  Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT. , 2018, The Science of the total environment.

[62]  Jian-bing Peng,et al.  Development characteristics and formation analysis of Baixiang earth fissure on North China plain , 2018, Bulletin of Engineering Geology and the Environment.

[63]  A. Zhu,et al.  Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. , 2018, The Science of the total environment.

[64]  Jiahao Deng,et al.  Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models , 2018, Landslides.

[65]  Jian-bing Peng,et al.  Development characteristics and mechanisms of the Taigu–Qixian earth fissure group in the Taiyuan basin, China , 2018, Environmental Earth Sciences.

[66]  Zhong Lu,et al.  Deformation at longyao ground fissure and its surroundings, north China plain, revealed by ALOS PALSAR PS-InSAR , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[67]  Chengsheng Yang,et al.  MONITORING OF THE GROUND FISSURE ACTIVITY WITHIN YUNCHENG BASIN BY TIME SERIES INSAR , 2018 .

[68]  B. Choubin,et al.  Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches , 2018, Environmental Earth Sciences.

[69]  Wen-Chieh Cheng,et al.  Investigation into geohazards during urbanization process of Xi’an, China , 2018, Natural Hazards.

[70]  Alireza Soffianian,et al.  Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape , 2018 .

[71]  C. Janna,et al.  A Novel Approach to Model Earth Fissure Caused by Extensive Aquifer Exploitation and its Application to the Wuxi Case, China , 2018 .

[72]  F. Moore,et al.  Geochemical sources, hydrogeochemical behavior, and health risk assessment of fluoride in an endemic fluorosis area, central Iran. , 2018, Chemosphere.

[73]  Tsung-Yu Lee,et al.  Assessing typhoon damages to Taiwan in the recent decade: return period analysis and loss prediction , 2018, Natural Hazards.

[74]  Vivek Kumar,et al.  Assessing the feasibility of integrating ecosystem-based with engineered water resource governance and management for water security in semi-arid landscapes: A case study in the Banas catchment, Rajasthan, India. , 2018, The Science of the total environment.

[75]  Yao Du,et al.  Review: Water–rock interactions and related eco-environmental effects in typical land subsidence zones of China , 2018, Hydrogeology Journal.

[76]  Zhi-hua Chen,et al.  Mechanism of groundwater inrush hazard caused by solution mining in a multilayered rock-salt-mining area: a case study in Tongbai, China , 2018 .

[77]  Jin‐Yong Lee,et al.  Current water uses, related risks, and management options for Seoul megacity, Korea , 2018, Environmental Earth Sciences.

[78]  Jian-bing Peng,et al.  Classification, grading criteria and quantitative expression of earth fissures: a case study in Daming Area, North China Plain , 2018 .

[79]  K. Richards,et al.  Hydrogeological characteristics influencing the occurrence of pesticides and pesticide metabolites in groundwater across the Republic of Ireland. , 2017, The Science of the total environment.

[80]  Davide Notti,et al.  Multiband PSInSAR and long-period monitoring of land subsidence in a strategic detrital aquifer (Vega de Granada, SE Spain): An approach to support management decisions , 2017 .

[81]  M. Shirzaei,et al.  Aquifer Mechanical Properties and Decelerated Compaction in Tucson, Arizona , 2017 .

[82]  M. Rossi,et al.  Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods , 2017, Theoretical and Applied Climatology.

[83]  S. Shabani Modelling and mapping of soil damage caused by harvesting in Caspian forests (Iran) using CART and RF data mining techniques , 2017 .

[84]  H. Llanos,et al.  Estimación de la vulnerabilidad del acuífero del valle de Toluca mediante la combinación de un método paramétrico y el transporte advectivo. , 2017 .

[85]  Jun Li,et al.  Advanced Spectral Classifiers for Hyperspectral Images: A review , 2017, IEEE Geoscience and Remote Sensing Magazine.

[86]  Bin Shi,et al.  FBG-Based Monitoring of Geohazards: Current Status and Trends , 2017, Sensors.

[87]  H. Pourghasemi,et al.  Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models , 2017, Water Resources Management.

[88]  N. H. Williams,et al.  Arsenic contamination of drinking water in Ireland: A spatial analysis of occurrence and potential risk. , 2017, The Science of the total environment.

[89]  Md Bayzidul Islam,et al.  A regional groundwater-flow model for sustainable groundwater-resource management in the south Asian megacity of Dhaka, Bangladesh , 2017, Hydrogeology Journal.

[90]  James J. Yoo,et al.  The varying impact of land subsidence and earth fissures on residential property values in Maricopa County – a quantile regression approach , 2017 .

[91]  C.Y. Jim,et al.  Do vegetated rooftops attract more mosquitoes? Monitoring disease vector abundance on urban green roofs. , 2016, The Science of the total environment.

[92]  Zhengdong Deng,et al.  Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms , 2016 .

[93]  Matthias Schonlau,et al.  Support Vector Machines , 2016 .

[94]  Jian-bing Peng,et al.  Characteristics and mechanism of the Longyao ground fissure on North China Plain, China , 2016 .

[95]  Gang Mei,et al.  Predicting the distribution of ground fissures and water-conducted fissures induced by coal mining: a case study , 2016, SpringerPlus.

[96]  J. K. Adamson,et al.  Summary of groundwater resources in Haiti , 2016 .

[97]  Shujun Ye,et al.  Progression and mitigation of land subsidence in China , 2016, Hydrogeology Journal.

[98]  B. Conway Land subsidence and earth fissures in south-central and southern Arizona, USA , 2016, Hydrogeology Journal.

[99]  Jichun Wu,et al.  Mechanisms for earth fissure formation due to groundwater extraction in the Su-Xi-Chang area, China , 2016, Bulletin of Engineering Geology and the Environment.

[100]  A. Ghazifard,et al.  Effects of groundwater withdrawal on land subsidence in Kashan Plain, Iran , 2016, Bulletin of Engineering Geology and the Environment.

[101]  Qiang Xu,et al.  Complex Deformation Monitoring over the Linfen-Yuncheng Basin (China) with Time Series InSAR Technology , 2016, Remote. Sens..

[102]  Bahram Choubin,et al.  Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals , 2016 .

[103]  Jun Yu,et al.  Investigations of Changjing earth fissures, Jiangyin, Jiangsu, China , 2016, Environmental Earth Sciences.

[104]  O. Kisi,et al.  Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution , 2016 .

[105]  M. Parise Karst Geo-hazards: Causal Factors and Management Issues , 2016 .

[106]  E. Levina,et al.  Prediction models for network-linked data , 2016, The Annals of Applied Statistics.

[107]  C. Scawthorn,et al.  Statistical Modeling of Fire Occurrence Using Data from the Tōhoku, Japan Earthquake and Tsunami , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[108]  Penélope López-Quiroz,et al.  On the potential of time series InSAR for subsidence and ground rupture evaluation: application to Texcoco and Cuautitlan–Pachuca subbasins, northern Valley of Mexico , 2015, Natural Hazards.

[109]  Matteo Albano,et al.  Land subsidence, Ground Fissures and Buried Faults: InSAR Monitoring of Ciudad Guzmán (Jalisco, Mexico) , 2015, Remote. Sens..

[110]  Duncan Fyfe Gillies,et al.  A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data , 2015, Adv. Bioinformatics.

[111]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[112]  Zhong Lu,et al.  Land subsidence and ground fissures in Xi'an, China 2005-2012 revealed by multi-band InSAR time-series analysis , 2014 .

[113]  Jun Yu,et al.  Occurrence assessment of earth fissure based on genetic algorithms and artificial neural networks in Su-Xi-Chang land subsidence area, China , 2014, Geosciences Journal.

[114]  N. Maerz,et al.  Earth Fissures in Wadi Najran, Kingdom of Saudi Arabia , 2014, Natural Hazards.

[115]  Michael Smithson,et al.  Generalized Linear Models for Categorical and Continuous Limited Dependent Variables , 2013 .

[116]  C. Zhu,et al.  Hybrid of Genetic Algorithm and Simulated Annealing for Support Vector Regression Optimization in Rainfall Forecasting , 2013, Int. J. Comput. Intell. Appl..

[117]  Jian-bing Peng,et al.  Physical simulation of ground fissures triggered by underground fault activity , 2013 .

[118]  M. Dehghani,et al.  Investigation of land subsidence in southern Mahyar Plain in Esfahan province, Iran , 2012 .

[119]  M. Budhu,et al.  Earth fissure formation from groundwater pumping and the influence of a stiff upper cemented layer , 2012 .

[120]  Shahram,et al.  Land Subsidence and Fissuring Due to Ground Water Withdrawal in Yazd-Ardakan Basin, Central Iran , 2010 .

[121]  G. Y. Wang,et al.  Earth fissures in Jiangsu Province, China and geological investigation of Hetang earth fissure , 2010 .

[122]  A. Sepehr,et al.  Investigation of wind erosion process for estimation, prevention, and control of DSS in Yazd–Ardakan plain , 2009, Environmental monitoring and assessment.

[123]  Jichun Wu,et al.  Land subsidence and earth fissures due to groundwater withdrawal in the Southern Yangtse Delta, China , 2008 .

[124]  J. Nelder,et al.  Double hierarchical generalized linear models (with discussion) , 2006 .

[125]  L. Ayalew,et al.  Ground cracks in Ethiopian Rift Valley: facts and uncertainties , 2004 .

[126]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[127]  Zhuping Sheng,et al.  Mechanisms of Earth Fissuring Caused by Groundwater Withdrawal , 2003 .

[128]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[129]  David G. T. Denison,et al.  Bayesian MARS , 1998, Stat. Comput..

[130]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[131]  Lynn E. Johnson,et al.  Assessment of Quantitative Precipitation Forecasts , 1998 .

[132]  Charles B. Roosen,et al.  An introduction to multivariate adaptive regression splines , 1995, Statistical methods in medical research.

[133]  Gordon J. Esplin,et al.  Approximate explicit solution to the general line source problem , 1995 .

[134]  N R Temkin,et al.  Classification and regression trees (CART) for prediction of function at 1 year following head trauma. , 1995, Journal of neurosurgery.

[135]  Deng Aisong,et al.  Land subsidence, sinkhole collapse and earth fissure occurrence and control in China , 1994 .

[136]  J. Friedman Multivariate adaptive regression splines , 1990 .

[137]  R. Redfield,et al.  Measurement of the false positive rate in a screening program for human immunodeficiency virus infections. , 1988, The New England journal of medicine.

[138]  Thomas L. Holzer,et al.  Earth fissures and localized differential subsidence , 1981 .

[139]  S. Chatterjee,et al.  Regression Analysis by Example , 1979 .

[140]  Herman Bouwer,et al.  Land Subsidence and Cracking Due to Ground‐Water Depletiona , 1977 .

[141]  T. Bakhshpoori,et al.  Soft computing-based slope stability assessment: A comparative study , 2018 .

[142]  M. Saber IMPLICATIONS OF LAND SUBSIDENCE DUE TO GROUNDWATER OVER-PUMPING: MONITORING METHODOLOGY USING GRACE DATA , 2018 .

[143]  Binh Thai Pham,et al.  Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers , 2018, Ecol. Informatics.

[144]  Seyed Amir Naghibi,et al.  GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran , 2015, Environmental Monitoring and Assessment.

[145]  M. Momeni,et al.  Land Subsidence in Mahyar Plain, Central Iran, Investigated Using Envisat SAR Data , 2015 .

[146]  Wang Qi-ya Numerical simulation and layerwise mark monitoring of land subsidence and ground fissures of typical section in Xi'an , 2014 .

[147]  Luo Zu-jiang,et al.  Simulating and forecasting of groundwater exploitation,land subsidence and ground fissure in Cangzhou City , 2013 .

[148]  M. Hernández-Marín,et al.  On the mechanisms for earth fissuring in Las Vegas valley: a numerical analysis of pumping-induced deformation and stress , 2010 .

[149]  N. Khaleghpanah,et al.  Physico-chemical characteristics and clay mineralogy composition of selected soils in arid and semiarid regions of Iran , 2010 .

[150]  RANJIT KUMAR PAUL,et al.  MULTICOLLINEARITY : CAUSES , EFFECTS AND REMEDIES , 2008 .

[151]  D. Basak,et al.  Support Vector Regression , 2008 .

[152]  N. Breslow,et al.  Generalized Linear Models: Checking Assumptions and Strengthening Conclusions , 2022 .

[153]  L. Breiman Random Forests , 2001, Machine Learning.

[154]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[155]  Ehl Emile Aarts,et al.  Simulated annealing and Boltzmann machines , 2003 .

[156]  G. Tosatti,et al.  Engineering geology, geotechnics and hydrogeology in environmental management: Northern Italian experiences , 1992 .

[157]  T. Péwé Land subsidence and earth-fissure formation caused by groundwater withdrawal in Arizona; a review. , 1990 .

[158]  S. R. Anderson Potential for aquifer compaction, land subsidence, and earth fissures in the Tucson basin, Pima County, Arizona , 1987 .

[159]  R. L. Laney,et al.  Land subsidence and earth fissures caused by groundwater depletion in Southern Arizona, U.S.A. , 1986 .

[160]  R. R. Hocking,et al.  The regression dilemma , 1983 .

[161]  G. Brier,et al.  Some applications of statistics to meteorology , 1958 .