John P. Tiefenbacher
John P. Tiefenbacher
Professor of Geography, Texas State University
Verified email at - Homepage
Cited by
Cited by
Improving prediction of water quality indices using novel hybrid machine-learning algorithms
DT Bui, K Khosravi, J Tiefenbacher, H Nguyen, N Kazakis
Science of the Total Environment 721, 137612, 2020
Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods
O Rahmati, B Choubin, A Fathabadi, F Coulon, E Soltani, H Shahabi, ...
Science of the Total Environment 688, 855-866, 2019
A comparative study of environmental knowledge, attitudes and behaviors among university students in China
X He, T Hong, L Liu, J Tiefenbacher
International Research in Geographical and Environmental Education 20 (2 …, 2011
Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping
P Yariyan, S Janizadeh, T Van Phong, HD Nguyen, R Costache, ...
Water Resources Management 34, 3037-3053, 2020
Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)
HR Pourghasemi, S Pouyan, B Heidari, Z Farajzadeh, SRF Shamsi, ...
International Journal of Infectious Diseases 98, 90-108, 2020
En-gendered fears: Femininity and technological risk perception
SL Cutter, J Tiefenbacher, WD Solecki
Industrial Crisis Quarterly 6 (1), 5-22, 1992
Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling
HR Pourghasemi, A Gayen, R Lasaponara, JP Tiefenbacher
Environmental research 184, 109321, 2020
Attributes of repeat visitors to small tourist-oriented communities
JP Tiefenbacher, FA Day, JA Walton
The Social Science Journal 37 (2), 299-308, 2000
The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers
DD Moghaddam, O Rahmati, M Panahi, J Tiefenbacher, H Darabi, ...
Catena 187, 104421, 2020
Gully headcut susceptibility modeling using functional trees, naīve Bayes tree, and random forest models
M Hosseinalizadeh, N Kariminejad, W Chen, HR Pourghasemi, ...
Geoderma 342, 1-11, 2019
Urban flood modeling using deep-learning approaches in Seoul, South Korea
X Lei, W Chen, M Panahi, F Falah, O Rahmati, E Uuemaa, Z Kalantari, ...
Journal of Hydrology 601, 126684, 2021
Identification of soil erosion-susceptible areas using fuzzy logic and analytical hierarchy process modeling in an agricultural watershed of Burdwan district, India
S Saha, A Gayen, HR Pourghasemi, JP Tiefenbacher
Environmental Earth Sciences 78 (23), 649, 2019
Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future
S Janizadeh, SC Pal, A Saha, I Chowdhuri, K Ahmadi, S Mirzaei, ...
Journal of Environmental Management 298, 113551, 2021
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility
A Arabameri, S Saha, J Roy, JP Tiefenbacher, A Cerda, T Biggs, ...
Science of the total environment 726, 138595, 2020
Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?
HR Pourghasemi, A Gayen, M Edalat, M Zarafshar, JP Tiefenbacher
Geoscience Frontiers 11 (4), 1203-1217, 2020
River water salinity prediction using hybrid machine learning models
AM Melesse, K Khosravi, JP Tiefenbacher, S Heddam, S Kim, A Mosavi, ...
Water 12 (10), 2951, 2020
A machine learning framework for multi-hazards modeling and mapping in a mountainous area
S Yousefi, HR Pourghasemi, SN Emami, S Pouyan, S Eskandari, ...
Scientific Reports 10 (1), 12144, 2020
Access to healthcare and disparities in colorectal cancer survival in Texas
N Wan, FB Zhan, Y Lu, JP Tiefenbacher
Health & place 18 (2), 321-329, 2012
Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model
A Arabameri, JP Tiefenbacher, T Blaschke, B Pradhan, D Tien Bui
Remote Sensing 12 (5), 874, 2020
Novel ensemble of MCDM-artificial intelligence techniques for groundwater-potential mapping in arid and semi-arid regions (Iran)
A Arabameri, S Lee, JP Tiefenbacher, PTT Ngo
Remote Sensing 12 (3), 490, 2020
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