Amir Naghibi
Researcher
Amir Naghibi received his Ph.D. in Watershed Management Sciences and Engineering from Tarbiat Modares University, Iran, in 2019. Then, he worked on a postdoc project at the Faculty of Engineering and Centre for Advanced Middle Eastern Studies (CMES), Lund University where he focused on applying remote sensing and machine learning algorithms to manage groundwater resources. He currently works as an associate senior lecturer in the division of Water Resources Engineering. His main research interests include but are not limited to:
- Developing AI-based frameworks for modeling and solving water-related issues.
- Modeling water resources quantity and quality as well as spatial and temporal modeling of natural hazards such as landslides, dust storms, and floods with interdisciplinary approaches with a focus on the Middle East.
Publications
Displaying of publications. Sorted by year, then title.
Examining the Role of the Main Terrestrial Factors Won the Seasonal Distribution of Atmospheric Carbon Dioxide Concentration over Iran
Seyed Mohsen Mousavi, Naghmeh Mobarghaee Dinan, Saeed Ansarifard, Faezeh Borhani, Keyvan Ezimand, et al.
(2023) Journal of the Indian Society of Remote Sensing
Journal articleInSAR-AI-Based Approach for Groundwater Level Prediction in Arid Regions
Behshid Khodaei, Hossein Hashemi, Seyed Amir Naghibi, Ronny Berndtsson
(2023)
Conference paperOptimal Landfill Site Selection for Solid Waste of Three Municipalities Based on Boolean and Fuzzy Methods: A Case Study in Kermanshah Province, Iran
Seyed Mohsen Mousavi, Golnaz Darvishi, Naghmeh Mobarghaee Dinan, Seyed Amir Naghibi
(2022) Land, 11
Journal articleAn Integrated InSAR-Machine Learning Approach for Ground Deformation Rate Modeling in Arid Areas
Seyed Amir Naghibi, Behshid Khodaei, Hossein Hashemi
(2022) Journal of Hydrology, 608
Journal articleDevelopment of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood
Hamid Darabi, Omid Rahmati, Seyed Amir Naghibi, Farnoush Mohammadi, Ebrahim Ahmadisharaf, et al.
(2022) Geocarto International, 37 p.5716-5741
Journal articleHuman-induced arsenic pollution modeling in surface waters : An integrated approach using machine learning algorithms and environmental factors
Maziar Mohammadi, Seyed Amir Naghibi, Alireza Motevalli, Hossein Hashemi
(2022) Journal of Environmental Management, 305
Journal articleAPG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies
Seyed Amir Naghibi, Hossein Hashemi, Biswajeet Pradhan
(2021) Geoscience Frontiers, 12
Journal articleApplication of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data
Ehsan Kamali Maskooni, Seyed Amir Naghibi, Hossein Hashemi, Ronny Berndtsson
(2020) Remote Sensing, 12
Journal articleDevelopment of novel hybridized models for urban flood susceptibility mapping
Omid Rahmati, Hamid Darabi, Mahdi Panahi, Zahra Kalantari, Seyed Amir Naghibi, et al.
(2020) Scientific Reports, 10
Journal articleApplication of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors
Seyed Amir Naghibi, Hossein Hashemi, Ronny Berndtsson, Saro Lee
(2020) Journal of Hydrology, 589
Journal articleInverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater
Alireza Motevalli, Seyed Amir Naghibi, Hossein Hashemi, Ronny Berndtsson, Biswajeet Pradhan, et al.
(2019) Journal of Cleaner Production, 228 p.1248-1263
Journal articleCartografía del potencial de agua subterránea utilizando un nuevo modelo de conjuntos de minería de datos
Mojtaba Dolat Kordestani, Seyed Amir Naghibi, Hossein Hashemi, Kourosh Ahmadi, Bahareh Kalantar, et al.
(2019) Hydrogeology Journal, 27 p.211-224
Journal article