J. Tiefenbacher

发表

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Thomas Blaschke, Biswajeet Pradhan, Dieu Tien Bui, 2019, Water.

River Water Salinity Prediction Using Hybrid Machine Learning Models

B. Pham, K. Khosravi, A. Mosavi, 2020, Water.

Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling.

R. Lasaponara, H. Pourghasemi, J. Tiefenbacher, 2020, Environmental research.

Assessment of Gini-, entropy- and ratio-based classification trees for groundwater potential modelling and prediction

D. Bui, Omid Rahmati, A. Azareh, 2020, Geocarto International.

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

H. Pourghasemi, J. Tiefenbacher, Amiya Gayen, 2020 .

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

H. Pourghasemi, J. Tiefenbacher, Nitheshnirmal Sadhasivam, 2020, PloS one.

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

H. Pourghasemi, M. H. Tarazkar, J. Tiefenbacher, 2020, International Journal of Infectious Diseases.

Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models

H. Pourghasemi, J. Tiefenbacher, Nitheshnirmal Sadhasivam, 2020, medRxiv.

A methodological comparison of head-cut based gully erosion susceptibility models: Combined use of statistical and artificial intelligence

B. Pradhan, D. Bui, L. Lombardo, 2020 .

Gully headcut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models

Wei Chen, H. Pourghasemi, Narges Kariminejad, 2019, Geoderma.

Approaches to Managing Disaster-assessing Hazards, Emergencies and Disaster Impacts

J. Tiefenbacher, John Tiefenbacher, 2014 .

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods.

Frederic Coulon, Himan Shahabi, Baharin Bin Ahmad, 2019, The Science of the total environment.

A simple method to evaluate groundwater vulnerability in urbanizing agricultural regions.

J. Tiefenbacher, T. Takaro, E. A. Ojeda Olivares, 2020, Journal of environmental management.

Attributes of repeat visitors to small tourist-oriented communities

John P. Tiefenbacher, J. Tiefenbacher, F. Day, 2000 .

Assessing the susceptibility of schools to flood events in Iran

H. Pourghasemi, Omid Rahmati, S. Yousefi, 2020, Scientific Reports.

Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia.

Zahra Kalantari, Omid Rahmati, Mahdi Panahi, 2019, The Science of the total environment.

Comparison analytic network and analytical hierarchical process approaches with feature selection algorithm to predict groundwater quality

H. Pourghasemi, M. Mokarram, J. Tiefenbacher, 2019, Environmental Earth Sciences.

Identifying sources of dust aerosol using a new framework based on remote sensing and modelling.

Seid Saeid Ghiasi, D. Tien Bui, F. Coulon, 2020, The Science of the total environment.

The barriers impeding precautionary behaviours by undocumented immigrants in emergencies: The Hurricane Ike experience in Houston, Texas, USA

J. Tiefenbacher, Sylvia N. Wilson, 2012 .

Throwaway societies: a field survey of the quantity, nature and distribution of litter in New Jersey

S. Cutter, J. Tiefenbacher, W. Solecki, 1991 .

Using Dempster–Shafer theory to model earthquake events

H. Pourghasemi, M. Mokarram, J. Tiefenbacher, 2020, Natural Hazards.

TET: An automated tool for evaluating suitable check-dam sites based on sediment trapping efficiency

Seid Saeid Ghiasi, D. Bui, D. Tien Bui, 2020 .

Application of the novel state-of-the-art soft computing techniques for groundwater potential assessment

Muhammad Al-Amin Hoque, R. Costache, A. Cerda, 2022, Arabian Journal of Geosciences.

Using computational-intelligence algorithms and remote sensing data to optimize the locations of check dams to control sediment and runoff in Kandolus watershed, Mazandaran, Iran

Shahab S. Band, S. Pal, Saeid Janizadeh, 2022, Geocarto International.

CHEMICAL HAZARDS IN URBAN AMERICA

S. Cutter, J. Tiefenbacher, 1991 .

Landscape, development, technology and drivers: The geography of drownings associated with automobiles in Texas floods, 1950–2004

J. Tiefenbacher, Lauren Z. Maples, 2009 .

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.

Phuong Thao Thi Ngo, B. Pradhan, A. Cerda, 2020, The Science of the total environment.

Spatial analysis of environmental factors influencing dust sources in the east of Iran using a new active-learning approach

Seid Saeid Ghiasi, J. Tiefenbacher, M. Amiri, 2022, Geocarto International.

Social Power and the Urbanization of Water: Flows of Power

J. Tiefenbacher, 2005 .

Application of the novel state-of-the-art soft computing techniques for groundwater potential assessment

Muhammad Al-Amin Hoque, R. Costache, A. Cerda, 2022, Arabian Journal of Geosciences.

La Frontera Química: Toxic emissions and spills along the U.S.‐Mexican Border

J. Tiefenbacher, 1998 .

Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger

H. Pourghasemi, S. Eskandari, J. Tiefenbacher, 2020 .

Fire Susceptibility Mapping in the Northeast Forests and Rangelands of Iran using New and Ensemble Data Mining Models

H. Pourghasemi, S. Eskandari, J. Tiefenbacher, 2021 .

The impact of land use and land cover changes on soil erosion in western Iran

H. Khosravi, J. Tiefenbacher, Khalil Habashi, 2021, Natural Hazards.

An evaluation of alternative statistical models for predicting habitat suitability for weeds

H. Pourghasemi, J. Tiefenbacher, M. Edalat, 2022, Weed Research.

Themes of U.S. Wine Advertising and the Use of Geography and Place to Market Wine

J. Tiefenbacher, 2013 .

Environmental Review: Persistent, Bioaccumulative, and Toxic Chemicals in the US-Mexico Borderlands: A Regional Assessment of the Situation

J. Tiefenbacher, 2000 .

Shade moderates the drought stress on saplings of Beneh (Pistacia atlantica Desf. subsp. mutica) in semiarid areas of Iran

S. Eskandari, J. Tiefenbacher, D. Azadfar, 2022, Environmental Science and Pollution Research.

The Semiofoodscape of Wine: The Changing Global Landscape of Wine Culture and the Language of Making, Selling, and Drinking Wine

J. Tiefenbacher, C. Townsend, 2019, Handbook of the Changing World Language Map.

Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes

Quoc Bao Pham, Duong Tran Anh, Asish Saha, 2021, Geocarto International.

Spatial mapping Zataria multiflora using different machine-learning algorithms

H. Pourghasemi, J. Tiefenbacher, M. Edalat, 2022, CATENA.

Optimization of statistical and machine learning hybrid models for groundwater potential mapping

Q. Pham, N. T. Linh, J. Tiefenbacher, 2021, Geocarto International.

A machine learning framework for multi-hazards modeling and mapping in a mountainous area

Hamid Reza Pourghasemi, Saleh Yousefi, Sayed Naeim Emami, 2020, Scientific Reports.

The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers

Dieu Tien Bui, Hamid Darabi, Omid Rahmati, 2020, CATENA.

Optimizing collapsed pipes mapping: Effects of DEM spatial resolution

Hamid Reza Pourghasemi, Mauro Rossi, John P. Tiefenbacher, 2020 .

Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)

Saro Lee, Phuong-Thao Thi Ngo, Alireza Arabameri, 2020, Remote. Sens..

How do data-mining models consider arsenic contamination in sediments and variables importance?

Hamid Reza Pourghasemi, Prosun Bhattacharya, Alireza Motevalli, 2019, Environmental Monitoring and Assessment.

Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future.

A. Mosavi, Saeid Janizadeh, K. Ahmadi, 2021, Journal of environmental management.

Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping

Romulus Costache, Biswajeet Pradhan, Binh Thai Pham, 2020, Water Resources Management.

Identification of morphometric features of alluvial fan and basins in predicting the erosion levels using ANN

H. Pourghasemi, M. Mokarram, J. Tiefenbacher, 2022, Environmental Earth Sciences.

Selecting potential locations for groundwater recharge by means of remote sensing and GIS and weighting based on Boolean logic and analytic hierarchy process

S. Shojaei, Z. Kalantari, J. Tiefenbacher, 2021, Environmental Earth Sciences.

Identification of the most suitable afforestation sites by Juniperus excels specie using machine learning models: Firuzkuh semi-arid region, Iran

Romulus Costache, Mohammadtaghi Avand, Peyman Yariyan, 2021, Ecol. Informatics.

Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility

Biswajeet Pradhan, Phuong-Thao Thi Ngo, Alireza Arabameri, 2020, Remote. Sens..

A New Approach for Smart Soil Erosion Modeling: Integration of Empirical and Machine-Learning Models

Mohammadtaghi Avand, Ataollah Kavian, Maziar Mohammadi, 2021, Environmental Modeling & Assessment.

Assessment of Ensemble Models for Groundwater Potential Modeling and Prediction in a Karst Watershed

M. Zeleňáková, J. Tiefenbacher, Mohammadtaghi Avand, 2021, Water.

GIS-based spatial modeling of snow avalanches using four novel ensemble models.

Rahim Ali Abbaspour, Mohammadtaghi Avand, Peyman Yariyan, 2020, The Science of the total environment.

Spatial and temporal analysis of urban heat island using Landsat satellite images

H. Pourghasemi, S. Yousefi, J. Tiefenbacher, 2021, Environmental Science and Pollution Research.

Improving prediction of water quality indices using novel hybrid machine-learning algorithms.

Khabat Khosravi, Nerantzis Kazakis, Duie Tien Bui, 2020, The Science of the total environment.

Groundwater spring potential prediction using a deep-learning algorithm

Ebrahim Nohani, J. Tiefenbacher, S. Heddam, 2023, Acta Geophysica.

Identification of soil erosion-susceptible areas using fuzzy logic and analytical hierarchy process modeling in an agricultural watershed of Burdwan district, India

Hamid Reza Pourghasemi, Sunil Saha, John P. Tiefenbacher, 2019, Environmental Earth Sciences.

Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models

H. Pourghasemi, S. Eskandari, J. Tiefenbacher, 2021, Environmental Science and Pollution Research.

Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies

H. Pourghasemi, J. Tiefenbacher, Babak Zolghadr-Asli, 2021, Hydrological Sciences Journal.

Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms

D. Bui, Wei Chen, M. Panahi, 2021, Geocarto International.

En-gendered fears: femininity and technological risk perception

Susan L. Cutter, John P. Tiefenbacher, S. Cutter, 1992 .

Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence

Alireza Arabameri, Artemi Cerdà, John P. Tiefenbacher, 2019, Water.

Urban flood modeling using deep-learning approaches in Seoul, South Korea

Wei Chen, M. Panahi, Saro Lee, 2021 .

Spatial modeling of susceptibility to subsidence using machine learning techniques

H. Pourghasemi, M. Mohammady, J. Tiefenbacher, 2021, Stochastic Environmental Research and Risk Assessment.

Access to Health Care and Disparities in Colorectal Cancer Survival in Texas

Neng Wan, Yongmei Lu, J. Tiefenbacher, 2011, Health & place.

Research Article: Factors Affecting Hydrocarbon Vapor Transport from Leaking Petroleum Storage Tanks to Buildings

John P. Tiefenbacher, Christine W. Chandler, J. Tiefenbacher, 1999 .

Change detection in piping, gully head forms, and mechanisms

H. Pourghasemi, Narges Kariminejad, J. Tiefenbacher, 2021 .

Spatiotemporal monitoring of droughts in Iran using remote-sensing indices

H. Pourghasemi, M. Bordbar, J. Tiefenbacher, 2023, Natural Hazards.

A comparative study of environmental knowledge, attitudes and behaviors among university students in China

J. Tiefenbacher, John Tiefenbacher, Xueqin (Elaine) He, 2011 .

Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms

K. Khosravi, J. Tiefenbacher, A. Golkarian, 2022, Water Resources Management.

ENVIRONMENTAL EQUITY IN URBAN TEXAS: RACE, INCOME, AND PATTERNS OF ACUTE AND CHRONIC TOXIC AIR RELEASES IN METROPOLITAN COUNTIES

J. Tiefenbacher, R. Hagelman, 1999 .

National Geography Awareness Week 1987: An Assessment.

J. Tiefenbacher, B. Holcomb, 1989 .

Women, politics and place: Spatial patterns of representation in New Jersey

J. Tiefenbacher, J. Regulska, S. Fried, 1991 .

An optimization on machine learning algorithms for mapping snow avalanche susceptibility

J. Tiefenbacher, R. Ali Abbaspour, E. Omidvar, 2021, Natural Hazards.

Hybridized neural fuzzy ensembles for dust source modeling and prediction

Biswajeet Pradhan, Ravinesh C. Deo, Dieu Tien Bui, 2020 .

Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learning.

M. Khazaei, J. Tiefenbacher, Mohammadtaghi Avand, 2021, Journal of environmental management.

Prioritizing sub-watersheds based on soil-erosion potential by integrating RUSLE and game-theory algorithms

J. Tiefenbacher, Mohammadtaghi Avand, M. Mohammadi, 2023, Advances in Space Research.

Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis

H. Pourghasemi, J. Tiefenbacher, Babak Zolghadr-Asli, 2023, Water.

Using machine learning to predict processes and morphometric features of watershed

H. Pourghasemi, M. Mokarram, J. Tiefenbacher, 2023, Scientific reports.

Morphometry of AFs in upstream and downstream of floods in Gribayegan, Iran

H. Pourghasemi, M. Mokarram, J. Tiefenbacher, 2021, Natural Hazards.

Predicting Fault Locations based on Morphometric Features of Alluvial Fans and Basins using Artificial Neural Networks

H. Pourghasemi, M. Mokarram, J. Tiefenbacher, 2021 .

Evaluating novel hybrid models based on GIS for snow avalanche susceptibility mapping: A comparative study

Q. Pham, J. Tiefenbacher, M. Karami, 2021, Cold Regions Science and Technology.

Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree)

Hamid Reza Pourghasemi, Wei Chen, Narges Kariminejad, 2019, Geomorphology.

Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model

Thomas Blaschke, Biswajeet Pradhan, Dieu Tien Bui, 2020, Remote. Sens..

Identification of dust sources in a dust hot-spot area in Iran using multi-spectral Sentinel 2 data and deep learning artificial intelligence machine

J. Tiefenbacher, K. Nosrati, S. Maddah, 2022, Geocarto International.

Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility

Moslem Borji Hassangavyar, Q. Bach, Q. Pham, 2020, Geocarto International.

Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood

Hamid Darabi, Zahra Kalantari, Omid Rahmati, 2021, Geocarto International.

Testing Possible Scenario-Based Responses of Vegetation Under Expected Climatic Changes in Khuzestan Province

J. Tiefenbacher, A. Khoorani, M. Jafari, 2021, Air, Soil and Water Research.

The Role of Internacional Boundaries in Flood Hazard, Social Vulnerability and Disaster: A Bi-National Case Study of Ciudad Acuña, Coahuila and Del Rio, Texas

J. Tiefenbacher, 2006 .

Evaluating the Effectiveness of Public Participation Efforts by Environmental Agencies: Repermitting a Smelter in El Paso, Texas, USA

J. Tiefenbacher, Catherine A. Cunningham, 2008 .

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

R. Costache, A. Arabameri, H. Moayedi, 2021, Geomatics, Natural Hazards and Risk.

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Hamid Reza Pourghasemi, Thomas Blaschke, Biswajeet Pradhan, 2020, Sensors.

Hurricane evacuation behavior in domestic and international college students: The influences of environmental familiarity, expressed hurricane evacuation, and personal experience

Eric L. Samson, John P. Tiefenbacher, J. Tiefenbacher, 2007 .

Approaches to Disaster Management - Examining the Implications of Hazards, Emergencies and Disasters

J. Tiefenbacher, John Tiefenbacher, 2013 .

A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies

Mojtaba Naghdyzadegan Jahromi, H. Pourghasemi, J. Tiefenbacher, 2020, Environment, Development and Sustainability.

Prediction of drainage morphometry using a genetic landscape evolution algorithm

Hamid Reza Pourghasemi, John P. Tiefenbacher, Marzieh Mokarram, 2020, Geocarto International.

A LOCAL LEVEL QUANTITATIVE STUDY OF THE RELATIONSHIP BETWEEN FOREIGN-BORN POPULATIONS AND CARBON FOOTPRINT by

J. Tiefenbacher, H. Edwards, Chad L. Smith, 2020 .

American Hazardscapes: Regionalization of Hazards and Disasters

John P. Tiefenbacher, J. Tiefenbacher, 2003, The Professional Geographer.

GOOGLE EARTH ENGINE AND THE USE OF OPEN BIG DATA FOR ENVIRONMENTAL AND CLIMATE-CHANGE ASSESSMENTS: A KOSOVO CASE STUDY

J. Tiefenbacher, D. Sánchez, P. Ameti, 2023, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Omid Ghorbanzadeh, Dieu Tien Bui, Phuong-Thao Thi Ngo, 2019, Remote. Sens..

Provision of eucalyptus wood farming potential map in Iran: An application of land cover, ecological, climatic, hydrologic, and edaphic analysis in a GIS-based fuzzy AHP framework

H. Pourghasemi, S. Eskandari, J. Tiefenbacher, 2022, Ecological Indicators.

Individual and community perceptions of climate change in Lower Mustang, Nepal

Upendra B. Bom, J. Tiefenbacher, Shashidhar Belbase, 2022, Environment, Development and Sustainability.

Flood hazards, social vulnerability and societal risks in Russia

J. Tiefenbacher, N. Mavlyanova, V. Lipatov, 2023, Environment, Development and Sustainability.

Regional Cooperative Disaster Risk Management in Central Asian Borderlands

J. Tiefenbacher, N. Mavlyanova, V. Lipatov, 2021, Journal of Borderlands Studies.

Emergency Cooperation between the USA and Mexico in Disaster Management: Co-Dependency and Geopolitics

J. Tiefenbacher, R. Hagelman, 2008 .

Political and cultural contrasts in reporting about disasters: comparing United States and Chinese newspaper portrayals of bridge collapses

J. Tiefenbacher, X. He, 2008 .

Perspectives on Nature Conservation - Patterns, Pressures and Prospects

J. Tiefenbacher, John Tiefenbacher, 2012 .

Catchment-scale soil conservation: Using climate, vegetation, and topo-hydrological parameters to support decision making and implementation.

Moslem Borji, Aliakbar Nazari Samani, John P Tiefenbacher, 2019, The Science of the total environment.

Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling

A. Arabameri, H. Moayedi, Kaustuv Mukherjee, 2021, Stochastic Environmental Research and Risk Assessment.

Place, Perception, and Choice: Response of Citrus Growers to Freeze in California

J. Tiefenbacher, R. Hagelman, 2004 .

Introductory Chapter: Climates, Change, and Climate Change

J. Tiefenbacher, 2020 .