Works matching AU Pourghasemi, Hamid Reza
Results: 110
Author Correction: Using machine learning to predict processes and morphometric features of watershed.
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- 2023
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- Correction Notice
Using machine learning to predict processes and morphometric features of watershed.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-35634-2
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- Article
Mapping soil erosion susceptibility: a comparison of neural networks and fuzzy-AHP techniques.
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- Environmental Earth Sciences, 2024, v. 83, n. 19, p. 1, doi. 10.1007/s12665-024-11869-8
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- Article
Identification of morphometric features of alluvial fan and basins in predicting the erosion levels using ANN.
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- Environmental Earth Sciences, 2022, v. 81, n. 3, p. 1, doi. 10.1007/s12665-022-10219-w
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- Article
Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model.
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- Environmental Earth Sciences, 2021, v. 80, n. 8, p. 1, doi. 10.1007/s12665-021-09603-9
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- Article
Land-subsidence susceptibility zonation using remote sensing, GIS, and probability models in a Google Earth Engine platform.
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- Environmental Earth Sciences, 2020, v. 79, n. 21, p. 1, doi. 10.1007/s12665-020-09238-2
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- Article
Morphometric attributes-based soil erosion susceptibility mapping in Dnyanganga watershed of India using individual and ensemble models.
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- Environmental Earth Sciences, 2020, v. 79, n. 14, p. N.PAG, doi. 10.1007/s12665-020-09102-3
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A comparison of machine learning models for the mapping of groundwater spring potential.
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- Environmental Earth Sciences, 2020, v. 79, n. 10, p. 1, doi. 10.1007/s12665-020-08944-1
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- Article
Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models.
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- PLoS ONE, 2020, v. 15, n. 7, p. 1, doi. 10.1371/journal.pone.0236238
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- Article
Prediction of susceptible areas for groundwater recharge based on maximum entropy model.
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- Advanced Applied Geology, 2021, v. 11, n. 4, p. 723, doi. 10.22055/AAG.2020.29115.1967
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- Article
GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models.
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- Scandinavian Journal of Forest Research, 2016, v. 31, n. 1, p. 80, doi. 10.1080/02827581.2015.1052750
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- Article
Social networks' analysis of rural stakeholders in watershed management.
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- Environment, Development & Sustainability, 2021, v. 23, n. 12, p. 17535, doi. 10.1007/s10668-021-01399-9
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- Article
A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies.
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- Environment, Development & Sustainability, 2021, v. 23, n. 7, p. 10727, doi. 10.1007/s10668-020-01081-6
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- Article
Factors affecting methane emissions in OPEC member countries: does the agricultural production matter?
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- Environment, Development & Sustainability, 2021, v. 23, n. 5, p. 6734, doi. 10.1007/s10668-020-00887-8
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Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis.
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- Water (20734441), 2023, v. 15, n. 8, p. 1508, doi. 10.3390/w15081508
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Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios.
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- Water (20734441), 2019, v. 11, n. 11, p. 2319, doi. 10.3390/w11112319
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Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms.
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- Water (20734441), 2019, v. 11, n. 11, p. 2292, doi. 10.3390/w11112292
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A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping.
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- Water (20734441), 2019, v. 11, n. 10, p. 2076, doi. 10.3390/w11102076
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Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques.
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- Water (20734441), 2019, v. 11, n. 10, p. 2049, doi. 10.3390/w11102049
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- Article
Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models.
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- Environmental Science & Pollution Research, 2023, v. 30, n. 6, p. 16081, doi. 10.1007/s11356-022-23140-3
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Assessing and mapping distribution, area, and density of riparian forests in southern Iran using Sentinel-2A, Google earth, and field data.
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- Environmental Science & Pollution Research, 2022, v. 29, n. 52, p. 79605, doi. 10.1007/s11356-022-21478-2
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Assessment of groundwater vulnerability in an urban area: a comparative study based on DRASTIC, EBF, and LR models.
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- Environmental Science & Pollution Research, 2022, v. 29, n. 48, p. 72908, doi. 10.1007/s11356-022-20767-0
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Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt.
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- Environmental Science & Pollution Research, 2022, v. 29, n. 44, p. 66768, doi. 10.1007/s11356-022-20213-1
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A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method.
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- Environmental Science & Pollution Research, 2022, v. 29, n. 29, p. 43891, doi. 10.1007/s11356-022-18839-2
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Spatial modeling of land subsidence using machine learning models and statistical methods.
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- Environmental Science & Pollution Research, 2022, v. 29, n. 19, p. 28866, doi. 10.1007/s11356-021-18037-6
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- Article
Correction to: Prioritization of water erosion–prone sub-watersheds using three ensemble methods in Qareaghaj catchment, Southern Iran.
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- 2021
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- Correction Notice
Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models.
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- Environmental Science & Pollution Research, 2021, v. 28, n. 34, p. 47395, doi. 10.1007/s11356-021-13881-y
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Spatial and temporal analysis of urban heat island using Landsat satellite images.
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- Environmental Science & Pollution Research, 2021, v. 28, n. 30, p. 41439, doi. 10.1007/s11356-021-13693-0
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Investigation of plant contamination to Ni, Pb, Zn, and Cd and its relationship with spectral reflections.
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- Environmental Science & Pollution Research, 2021, v. 28, n. 28, p. 37830, doi. 10.1007/s11356-021-13394-8
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Prioritization of water erosion–prone sub-watersheds using three ensemble methods in Qareaghaj catchment, southern Iran.
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- Environmental Science & Pollution Research, 2021, v. 28, n. 28, p. 37894, doi. 10.1007/s11356-021-13300-2
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A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust.
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- Environmental Science & Pollution Research, 2020, v. 27, n. 33, p. 42022, doi. 10.1007/s11356-020-10168-6
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A comparative study between dynamic and soft computing models for sediment forecasting.
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- Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2021, v. 25, n. 16, p. 11005, doi. 10.1007/s00500-021-05834-x
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Assessment of land degradation using machine‐learning techniques: A case of declining rangelands.
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- Land Degradation & Development, 2021, v. 32, n. 3, p. 1452, doi. 10.1002/ldr.3794
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Soil loss tolerance in calcareous soils of a semiarid region: evaluation, prediction, and influential parameters.
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- Land Degradation & Development, 2020, v. 31, n. 15, p. 2156, doi. 10.1002/ldr.3597
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The potential of straw mulch as a nature‐based solution for soil erosion in olive plantation treated with glyphosate: A biophysical and socioeconomic assessment.
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- Land Degradation & Development, 2020, v. 31, n. 15, p. 1877, doi. 10.1002/ldr.3305
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GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran.
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- Land Degradation & Development, 2019, v. 30, n. 18, p. 2211, doi. 10.1002/ldr.3397
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Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm.
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- Land Degradation & Development, 2018, v. 29, n. 11, p. 4035, doi. 10.1002/ldr.3151
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- Article
SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment.
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- Applied Sciences (2076-3417), 2019, v. 9, n. 17, p. 3495, doi. 10.3390/app9173495
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Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms.
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- Applied Sciences (2076-3417), 2018, v. 8, n. 8, p. 1369, doi. 10.3390/app8081369
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A spatially explicit analytical framework to assess wildfire risks on brown bear habitat and corridors in conservation areas.
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- Fire Ecology, 2022, v. 18, n. 1, p. 1, doi. 10.1186/s42408-021-00125-0
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National flood susceptibility mapping in Saudi Arabia.
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- Earth Science Informatics, 2025, v. 18, n. 1, p. 1, doi. 10.1007/s12145-024-01510-4
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Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study.
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- Sensors (14248220), 2020, v. 20, n. 2, p. 1, doi. 10.3390/s20020335
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Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies.
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- Hydrological Sciences Journal/Journal des Sciences Hydrologiques, 2021, v. 66, n. 12, p. 1767, doi. 10.1080/02626667.2021.1944633
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Landslides and flood multi-hazard assessment using machine learning techniques.
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- Bulletin of Engineering Geology & the Environment, 2022, v. 81, n. 9, p. 1, doi. 10.1007/s10064-022-02874-x
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A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran).
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- Bulletin of Engineering Geology & the Environment, 2020, v. 79, n. 10, p. 5291, doi. 10.1007/s10064-020-01915-7
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Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions.
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- Bulletin of Engineering Geology & the Environment, 2019, v. 78, n. 6, p. 4017, doi. 10.1007/s10064-018-1403-6
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A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China.
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- Bulletin of Engineering Geology & the Environment, 2018, v. 77, n. 2, p. 647, doi. 10.1007/s10064-017-1010-y
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Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms.
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- Bulletin of Engineering Geology & the Environment, 2018, v. 77, n. 2, p. 611, doi. 10.1007/s10064-017-1004-9
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Modeling and assessing the effects of land use changes on runoff generation with the CLUE-s and WetSpa models.
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- Theoretical & Applied Climatology, 2018, v. 133, n. 1-2, p. 459, doi. 10.1007/s00704-017-2190-x
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A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS.
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- Theoretical & Applied Climatology, 2018, v. 131, n. 3-4, p. 967, doi. 10.1007/s00704-016-2022-4
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