Abolfazl Jaafari
Biographic Data
| ID | 6274269 |
|---|---|
| NAME | Abolfazl Jaafari |
| GIVEN NAMES | Abolfazl |
| FAMILY NAME | Jaafari |
| SIGNATURE | JAAFARI A |
| AFFILIATIONS | Agricultural Research & Education Organization |
| ORCID | 0000-0002-3441-6560 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 6 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
An explainable machine learning framework for sustainable land-use planning
Spatiotemporal dynamics of social vulnerability to natural hazards
A spatiotemporal analysis using expert-weighted indicators for assessing social resilience to natural hazards
Exploring factors influencing urban sprawl and land-use changes analysis using systematic points and random forest classification
Spatial modeling of forest stand susceptibility to logging operations
Shallow Landslide Susceptibility Mapping
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark alg…
Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, ro…
Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique
The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Rece…
Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images
The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in the water level of Lake Urmia using the space-borne remote sensing and GIS techniques. Therefore, m…
Planning road networks in landslide-prone areas
Decision-making for the selection of a best wood extraction method
Decision-making for the selection of a best wood extraction method
Planning road networks in landslide-prone areas
Shallow Landslide Susceptibility Mapping
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark alg…
Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, ro…
Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique
The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Rece…
Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images
The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in the water level of Lake Urmia using the space-borne remote sensing and GIS techniques. Therefore, m…
Spatial modeling of forest stand susceptibility to logging operations
Exploring factors influencing urban sprawl and land-use changes analysis using systematic points and random forest classification
A spatiotemporal analysis using expert-weighted indicators for assessing social resilience to natural hazards
Spatiotemporal dynamics of social vulnerability to natural hazards
An explainable machine learning framework for sustainable land-use planning
Computer Science (9 works) · Environmental Science (7 works) · Geography (7 works) · Artificial Intelligence (5 works) · Engineering (5 works) · Flood Risk Assessment and Management (5 works) · Forest Biomass Utilization and Management (4 works) · Land use (4 works) · Landslide (4 works) · Landslides and related hazards (4 works)