Hamid Reza Pourghasemi
Datos Biográficos
| ID | 7564545 |
|---|---|
| NOMBRE | Hamid Reza Pourghasemi |
| NOMBRES | Hamid Reza |
| APELLIDO | Pourghasemi |
| FIRMA | POURGHASEMI H R |
| AFILIACIONES | Shiraz University |
| ORCID | 0000-0003-2328-2998 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 7 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 7 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2020 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2021 |
| ÍNDICE H | 0 |
Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing
Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations we…
Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models
The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-le…
Assessment of land degradation using machine‐learning techniques
Increased use and increasing demands pose serious threats to rangelands. In this study, we document a pronounced downward trend in rangeland quality in the Alborz Mountains in Firozkuh County, Iran using analysis of three machine‐learning models (MLMs). A total of 1,147 transects were established to evaluate the rangeland quality trends from field data collected over a 7‐year period. Twelve independent conditional factors were analyzed for their …
A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies
Factors affecting methane emissions in Opec member countries
Social networks` analysis of rural stakeholders in watershed management
Soil loss tolerance in calcareous soils of a semiarid region
Predicting soil loss tolerance ( T ‐value) as a first and crucial step in assessing soil erosion using pedotransfer functions (PTFs) could save time and cost. Therefore, this study aimed to evaluate T ‐value and its influential parameters for calcareous soils of the Dorudzan Watershed, Fars Province, Iran, and to develop PTFs for its prediction using easily measureable soil properties. T ‐value was determined in 60 soil profiles based on the soil…
Sin obras prominentes en esta página.
Soil loss tolerance in calcareous soils of a semiarid region
Predicting soil loss tolerance ( T ‐value) as a first and crucial step in assessing soil erosion using pedotransfer functions (PTFs) could save time and cost. Therefore, this study aimed to evaluate T ‐value and its influential parameters for calcareous soils of the Dorudzan Watershed, Fars Province, Iran, and to develop PTFs for its prediction using easily measureable soil properties. T ‐value was determined in 60 soil profiles based on the soil…
Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing
Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations we…
Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models
The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-le…
Assessment of land degradation using machine‐learning techniques
Increased use and increasing demands pose serious threats to rangelands. In this study, we document a pronounced downward trend in rangeland quality in the Alborz Mountains in Firozkuh County, Iran using analysis of three machine‐learning models (MLMs). A total of 1,147 transects were established to evaluate the rangeland quality trends from field data collected over a 7‐year period. Twelve independent conditional factors were analyzed for their …
A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies
Factors affecting methane emissions in Opec member countries
Social networks` analysis of rural stakeholders in watershed management
Environmental Science (5 obras) · Geography (5 obras) · Mathematics (4 obras) · Computer Science (3 obras) · Ecology (3 obras) · Geology (3 obras) · Agriculture (2 obras) · Climate change (2 obras) · Econometrics (2 obras) · Economics (2 obras)