Date of Birth: 16.09.1983

Phone number: (+46) 0734787823

Nationality: Iran Resident: Sweden and South Korea Email address: panahi2012@yahoo.com

Webpage in Google Scholar: http://scholar.google.com/citations?user=94AVPFYAAAAJ&hl=en

Webpage in ResearchGate: https://www.researchgate.net/profile/Mahdi_Panahi2

h-index(Google scholar): 51

i10-index(Google scholar): 80 Citation(Google scholar):10300

EDUCATION           Ph.D. (02/2019 - 02/2023)

University: Kangwon National University (KNU), South Korea Major: Smart Regional Innovation (AI in Geoscience)

GPA: 99.09/100

Master's Degree (02/2007 - 02/2009)

University: Islamic Azad University, Tehran, Iran Major: Geophysics Engineering (Natural Hazard)

Bachelor's Degree (02/2003 - 1/2007) University: Islamic Azad University, Tehran, Iran Major: Geomatics (Survey) Engineering

ACADEMIC EMPLOYMENT         

Postdoctoral

Department of Geomatic (remote sensing), Tehran University (QS World University Rankings: 400), Tehran. 08/2025 - Present

Postdoctoral

Department of Physical Geography, Stockholm University (QS World University Rankings: 128), Sweden. 08/2024 – 8/2025

Researcher

Water Centre, KTH Royal Institute of Technology (QS World University Rankings: 74), Sweden. 08/2024 - Present

Postdoctoral

Department of Civil and Environmental Engineering, Yonsei University (QS World University Rankings: 56), South Korea.

02/2023 – 06/2024

Researcher

Department of Physical Geography, Stockholm University (QS World University Rankings: 118), Sweden. 06/2022 - 12/2022

Researcher

Department of GeoAI, Korea Institute of Geoscience and Mineral Resources (KIGAM), South Korea. 09/2018 - 02/2023

AWARDS                  Named among the list of the world’s top 2 percent scientists, released by Stanford University (2025 report).

Named among the list of the world’s top 2 percent scientists, released by Stanford University (2024 report). Named among the list of the world’s top 2 percent scientists, released by Stanford University (2023 report). Named among the list of the world’s top 2 percent scientists, released by Stanford University (2022 report).

Publication                     2025

77- Panahi, M., Rezaie, F., and Kalantari, Z., Comparing Predictive Performance of Optimized Machine Learning Techniques for Global Flood Susceptibility Analysis, Journal of Hydrology: Regional Studies, (Under review).

76- Panahi, M. and Rezaie, F., and Kalantari, Z., Global Landslide Susceptibility Mapping: Utilizing Optimized Support Vector Regression and Meta-heuristic Algorithms, Natural Hazard.

75- Panahi, M., Rezaie, F., Kalantari, Z. and Moeini, H., Comparative Analysis of Classification Ability of Deep Learning Models for Landslide Detection and Enhancing Susceptibility Mapping Accuracy using Optimized Convolutional Neural Network Algorithms, Engineering Applications of Artificial Intelligence (Under review).

74- Seo, J., Panahi, M., Kim, j., Bateni, S.M., and Kim, Y., Global snow water equivalent product derived from machine learning model trained with in situ measurement data, Earth System Science Data (ESSD) (Under review).

2024

73- Alesheikh, A.A., Chatrsimab, Z., Rezaie, F., Lee, S., Jafari, A. and Panahi, M., 2024. Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach. The Egyptian Journal of Remote Sensing and Space Sciences, (Impact Factor: 3.7).

https://doi.org/10.1016/j.ejrs.2024.03.004

72- Ghayur Sadigh, A., Alesheikh, A.A., Bateni, S.M., Jun, C., Lee, S., Nielson, J.R., Panahi, M. and Rezaie, F., 2024. Comparison of optimized data-driven models for landslide susceptibility mapping. Environment, Development and Sustainability, (Impact Factor: 4.5).

https://doi.org/10.1007/s10668-023-03212-1

71- Tran, T.T.K., Janizadeh, S., Bateni, S.M., Jun, C., Kim, D., Trauernicht, C., Rezaie, F., Giambelluca,

T.W. and Panahi, M., 2024. Improving the prediction of wildfire susceptibility on Hawaiʻi Island, Hawaiʻi, using explainable hybrid machine learning models. Journal of environmental management, (Impact Factor: 8).

https://doi.org/10.1016/j.jenvman.2023.119724

2023

70-Panahi, M., Khosravi, K., Rezaie, F., Kalantari, Z., Ferreira, C., 2023. A Country Wide Evaluation of Sweden's Spatial Flood Modeling with Optimized Convolutional Neural Network Algorithms, Earth's Future, (Impact Factor: 8).

https://doi.org/10.1029/2023EF003749

69- Rezaie, F., Panahi, M., Bateni, S.M., Kim, S., Lee, J., Lee, J., Yoo, J., Kim, H., Kim, S.W. and Lee, S., 2023. Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms. Environment International, (Impact Factor: 11.8). https://doi.org/10.1016/j.envint.2022.107724

68- Rezaie, F., Panahi, M., Bateni, S.M., Lee, S., Jun, C., Trauernicht, C. and Neale, C.M., 2023. Development of novel optimized deep learning algorithms for wildfire modeling: A case study of Maui, Hawai ‘i. Engineering Applications of Artificial Intelligence, (Impact Factor: 8). https://doi.org/10.1016/j.engappai.2023.106699

67- Golkarian, A., Khosravi, K., Panahi, M. and Clague, J.J., 2023. Spatial variability of soil water erosion: Comparing empirical and intelligent techniques. Geoscience Frontiers, (Impact Factor: 8.9). https://doi.org/10.1016/j.gsf.2022.101456

66- Paryani, S., Bordbar, M., Jun, C., Panahi, M., Bateni, S.M., Neale, C.M., Moeini, H. and Lee, S., 2023. Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran. Natural Hazards, (Impact Factor: 3.7).

https://doi.org/10.1007/s11069-022-05701-4

65- Ghayur Sadigh, A., Alesheikh, A.A., Bateni, S.M., Jun, C., Lee, S., Nielson, J.R., Panahi, M. and Rezaie, F., 2023. Comparison of optimized data-driven models for landslide susceptibility mapping. Environment, Development and Sustainability, (Impact Factor: 4.889).

https://doi.org/10.1016/j.cageo.2011.04.012

64- Aoulmi, Y., Marouf, N., Rasouli, K. and Panahi, M., 2023. Runoff Predictions in a Semiarid Watershed by Convolutional Neural Networks Improved with Metaheuristic Algorithms and Forced with Reanalysis and Climate Data. Journal of Hydrologic Engineering, (Impact Factor:2.4). https://doi.org/10.1061/JHYEFF.HEENG-5920

63- Tran, T.T.K., Bateni, S.M., Rezaie, F., Panahi, M., Jun, C., Trauernicht, C. and Neale, C.M., 2023. Enhancing predictive ability of optimized group method of data handling (GMDH) method for wildfire susceptibility mapping. Agricultural and Forest Meteorology, (Impact Factor: 6.2). https://doi.org/10.1016/j.agrformet.2023.109587

62- Jafari, A., Alesheikh, A.A., Rezaie, F., Panahi, M., Shahsavar, S., Lee, M.J. and Lee, S., 2023. Enhancing a convolutional neural network model for land subsidence susceptibility mapping using hybrid meta-heuristic algorithms. International Journal of Coal Geology, (Impact Factor: 5.689). https://doi.org/10.1016/j.coal.2023.104350

61- Shafapourtehrany, M., Rezaie, F., Jun, C., Heggy, E., Bateni, S.M., Panahi, M., Ö zener, H., Shabani,

F. and Moeini, H., 2023. Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms. Remote Sensing, (Impact Factor: 5). https://doi.org/10.3390/rs15184501

2022

60- Hamedi, H., Alesheikh, A. A., Panahi, M., & Lee, S. (2022). Landslide susceptibility mapping using deep learning models in Ardabil province, Iran. Stochastic Environmental Research and Risk Assessment, (Impact Factor: 3.82).

https://doi.org/10.1007/s00477-022-02263-6

59- Panahi, M., Rahmati, O., Kalantari, Z., Darabi, H., Rezaie, F., Moghaddam, D. D., ... & Lee, S. (2022). Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models. Journal of Hydrology, (Impact Factor: 5.72). https://doi.org/10.1016/j.jhydrol.2022.128001

58- Panahi, M., Khosravi, K., Golkarian, A., Roostaei, M., Barzegar, R., Omidvar, E., ... & Lee, S. (2022). A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning. Geocarto International, (Impact Factor: 4.889). https://doi.org/10.1080/10106049.2022.2086631

56- Rezaie, F., Panahi, M., Bateni, S. M., Jun, C., Neale, C. M., & Lee, S. (2022). Novel hybrid models by coupling support vector regression (SVR) with meta-heuristic algorithms (WOA and GWO) for flood susceptibility mapping. Natural Hazards, (Impact Factor: 5.19).

https://doi.org/10.1007/s11069-022-05424-6

55- Panahi, M., Rahmati, O., Rezaie, F., Lee, S., Mohammadi, F., & Conoscenti, C. (2022). Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates. Catena, (Impact Factor: 5.19).

https://doi.org/10.1016/j.catena.2021.105779

54- Fadhillah, M. F., Hakim, W. L., Panahi, M., Rezaie, F., Lee, C. W., & Lee, S. (2022). Mapping of landslide potential in Pyeongchang-gun, South Korea, using machine learning meta-based optimization algorithms. The Egyptian Journal of Remote Sensing and Space Science, (Impact Factor: 5.18). https://doi.org/10.1016/j.ejrs.2022.03.008

53- Panahi, M., Yariyan, P., Rezaie, F., Kim, S.W., Sharifi, A., Alesheikh, A.A., Lee, J., Lee, J., Kim, S., Yoo, J. and Lee, S., 2021. Spatial modeling of radon potential mapping using deep learning algorithms. Geocarto International, (Impact Factor: 4.889).

https://doi.org/10.1080/10106049.2021.2022011

52- Hakim, W. L., Rezaie, F., Nur, A. S., Panahi, M., Khosravi, K., Lee, C. W., & Lee, S. (2022). Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea. Journal of environmental management, (Impact Factor: 6.78). https://doi.org/10.1016/j.jenvman.2021.114367

51- Jaafari, A., Panahi, M., Mafi-Gholami, D., Rahmati, O., Shahabi, H., Shirzadi, A., Lee, S., Bui, D.T. and Pradhan, B., 2022. Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides. Applied Soft Computing, (Impact Factor: 6.72).

https://doi.org/10.1016/j.asoc.2021.108254

50- Rezaie, F., Panahi, M., Lee, J., Lee, J., Kim, S., Yoo, J., & Lee, S. (2021). Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms. Environmental Pollution, (Impact Factor: 8.07).

https://doi.org/10.1016/j.envpol.2021.118385

2021

49- Panahi, M., Dodangeh, E., Rezaie, F., Khosravi, K., Van Le, H., Lee, M.J., Lee, S. and Pham, B.T., 2021. Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. Catena, (Impact Factor: 5.19).

https://doi.org/10.1016/j.catena.2020.105114

48- Panahi, M., Khosravi, k., Ahmad, S., Panahi, S., Heddam, S., Melesse, A., Omidvar, E., Lee, C.H., 2021. Cumulative Infiltration and Infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran. Journal of Hydrology: Regional Studies, (Impact Factor: 3.64). https://doi.org/10.1016/j.ejrh.2021.100825

47- Panahi, M., Rahmati, O., Rezaie, F., Lee, S., Mohammadi, F.,

Conoscenti, C., 2021. Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates, Catena, (Impact Factor: 5.19). https://doi.org/10.1016/j.catena.2021.105779

46- Rezaie, F., Kim, S.W., Alizadeh, M., Panahi, M., Kim, H., Kim, S. and Lee, J., 2021. Application of Machine Learning Algorithms for Geogenic Radon Potential Mapping in Danyang-Gun, South Korea. Frontiers in Environmental Science, (Impact Factor: 4.88).

https://doi.org/10.3389/fenvs.2021.753028

45- Chen, Y., Chen, W., Rahmati, O., Falah, F., Kulakowski, D., Lee, S., Rezaie, F., Panahi, M., Bahmani, A., Darabi, H. and Haghighi, A.T., 2021. Toward the development of deep-learning analyses for snow avalanche releases in Mountain regions. Geocarto International, (Impact Factor: 4.88). https://doi.org/10.1080/10106049.2021.1986578

44- Lei, X., Chen, W., Panahi, M., Falah, F., Rahmati, O., Uuemaa, E., Kalantari, Z., Ferreira, C.S.S., Rezaie, F., Tiefenbacher, J.P. and Lee, S., 2021. Urban flood modeling using deep-learning approaches in Seoul, South Korea. Journal of Hydrology, (Impact Factor: 5.72). https://doi.org/10.1016/j.jhydrol.2021.126684

43- Li, Y., Chen, W., Rezaie, F., Rahmati, O., Davoudi Moghaddam, D., Tiefenbacher, J., Panahi, M., Lee, M.J., Kulakowski, D., Tien Bui, D. and Lee, S., 2021. Debris flows modeling using anthropogenic and geo-environmental factors: developing hybridized deep-learning algorithms. Geocarto International, (Impact Factor: 4.88).

https://doi.org/10.1080/10106049.2021.1912194

42-Pourghasemi, H.R., Amiri, M., Edalat, M., Ahrari, A.H., Panahi, M., Sadhasivam, N. and Lee, S., 2020. Assessment of Urban Infrastructures Exposed to Flood Using Susceptibility Map and Google Earth Engine.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, (Impact Factor: 3.82).

https://doi. org/10.1109/JSTARS.2020.3045278

2020

41- Panahi, M., Jaafari, A., Shirzadi, A., Shahabi, H., Rahmati, O., Omidvar, E., Lee, S. and Bui, D.T., 2020. Deep learning neural networks for spatially explicit prediction of flash flood probability. Geoscience Frontiers, (Impact Factor: 6.85).

https://doi.org/10.1016/j.gsf.2020.09.007

40- Panahi, M., Gayen, A., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms. Science of The Total Environment, (Impact Factor: 7.98). https://doi.org/10.1016/j.scitotenv.2020.139937

39- Panahi, M., Sadhasivam, N., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology, (Impact Factor: 4.405).

https://doi.org/10.1016/j.jhydrol.2020.125033

38- Balogun, A.L., Rezaie, F., Pham, Q.B., Gigović, L., Drobnjak, S., Aina, Y.A., Panahi, M., Yekeen, S.T. and Lee, S., 2020. Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with with GWO, BAT and COA algorithms. Geoscience Frontiers, (Impact Factor: 6.85

).

https://doi.org/10.1016/j.gsf.2020.10.009

37- Yekrangnia, M., Bakhshi, A., Ghannad, M.A. and Panahi, M., 2020. Risk assessment of confined unreinforced masonry buildings based on FEMA P-58 methodology: a case study—school buildings in Tehran. Bulletin of Earthquake Engineering, pp.1-42, (Impact Factor: 2.62). https://doi.org/10.1007/s10518-020-00990-1

36- Hooshangi, N., Alesheikh, A.A., Panahi, M. and Lee, S., 2020. USAR simulation system: presenting spatial strategies in agents' task allocation under uncertainties. Natural Hazards and Earth System Sciences Discussions, pp.1-18, (Impact Factor: 3.02).

https://doi.org/10.5194/nhess-21-3449-2021

35- Khosravi, K., Panahi, M, Golkarian, A, SD Keesstra, PM Saco, Bui, D. T, Lee, S., Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran, Journal of Hydrology, (Impact Factor: 4.405).

https://doi.org/10.1016/j.jhydrol.2020.125552

34- Dodangeh, E., Panahi, M., Rezaie, F., Lee, S., Bui, D. T., Lee, C. W., & Pradhan, B. (2020). Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search. Journal of Hydrology, (Impact Factor: 4.405). https://doi.org/10.1016/j.jhydrol.2020.125423

33- Chen, W., Chen, X., Peng, J., Panahi, M., & Lee, S. (2020). Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geoscience Frontiers, (Impact Factor: 6.85).

https://doi.org/10.1016/j.gsf.2020.07.012

32- Rahmati, O., Darabi, H., Panahi, M. et al. (2020). Development of novel hybridized models for urban flood susceptibility mapping. Scientific Reports, (Impact Factor: 3.99).

https://doi.org/10.1038/s41598-020-69703-7

31- Thi Ngo, P. T., Panahi, M., Khosravi, K., Ghorbanzadeh, O., Karimnejad, N., Cerdà, A. & Lee, S. (2020). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, (Impact Factor: 6.85).

https://doi.org/10.1016/j.gsf.2020.06.013

30- Syifa, M., Panahi, M. and Lee, C.W., 2020. Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA. Remote Sensing, (Impact Factor: 4.18).

https://doi.org/10.3390/rs12040623

29- Rahmati, O., Panahi, M., Ghiasi, S.S., Deo, R.C., Tiefenbacher, J.P., Pradhan, B., Jahani, A., Goshtasb, H., Kornejady, A., Shahabi, H. and Shirzadi, A., 2020. Hybridized neural fuzzy ensembles for dust source modeling and prediction. Atmospheric Environment, (Impact Factor: 4.02). https://doi.org/10.1016/j.atmosenv.2020.117320

28- Dodangeh, E., Choubin, B., Eigdir, A.N., Nabipour, N., Panahi, M., Shamshirband, S. and Mosavi, A., 2020. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Science of The Total Environment, (Impact Factor: 7.98). https://doi.org/10.1016/j.scitotenv.2019.135983

27- Moghaddam, D.D., Rahmati, O., Panahi, M., Tiefenbacher, J., Darabi, H., Haghizadeh, A., Haghighi, A.T., Nalivan, O.A. and Bui, D.T., 2020. The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. Catena, (Impact Factor: 5.19). https://doi.org/10.1016/j.catena.2019.104421

2019

26- Khosravi, K., Daggupati, P., Alami, M.T., Awadh, S.M., Ghareb, M.I., Panahi, M., Pham, B.T., Rezaie, F., Qi, C. and Yaseen, Z.M., 2019. Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq. Computers and Electronics in Agriculture, (Impact Factor: 3.17).

https://doi.org/10.1016/j.compag.2019.105041

25- Rahmati, O., Panahi, M., Kalantari, Z., Soltani, E., Falah, F., Dayal, K.S., Mohammadi, F., Deo, R.C., Tiefenbacher, J. and Bui, D.T., 2019. Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia. Science of The Total Environment, (Impact Factor: 7.93).

https://doi.org/10.1016/j.scitotenv.2019.134656

24- Pourghasemi, H.R., Gayen, A., Panahi, M., Rezaie, F. and Blaschke, T., 2019. Multi-hazard probability assessment and mapping in Iran. Science of The Total Environment, (Impact Factor: 7.93). https://doi.org/10.1016/j.scitotenv.2019.07.203

23- Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., Shirzadi, A., Shahabi, H., Panahi, S. and Costache, R., 2019. Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. Journal of environmental management, (Impact Factor: 5.86).

https://doi.org/10.1016/j.jenvman.2019.06.102

22- Ahmadlou, M., Karimi, M., Alizadeh, S., Shirzadi, A., Parvinnejhad, D., Shahabi, H. and Panahi, M., 2019. Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto International, (Impact Factor: 4.88).

https://doi.org/10.1080/10106049.2018.1474276

21- Chen, W., Panahi, M., Khosravi, K., Pourghasemi, H.R., Rezaie, F. and Parvinnezhad, D., 2019. Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. Journal of Hydrology, (Impact Factor: 4.04). https://doi.org/10.1016/j.jhydrol.2019.03.013

20- Jaafari, A., Panahi, M., Pham, B.T., Shahabi, H., Bui, D.T., Rezaie, F. and Lee, S., 2019. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf ptimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena, (Impact Factor: 5.19).

https://doi.org/10.1016/j.catena.2018.12.033

19- Jaafari, A., Zenner, E.K., Panahi, M. and Shahabi, H., 2019. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and forest meteorology, (Impact Factor: 5.31). https://doi.org/10.1016/j.agrformet.2018.12.015

18- Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A., Pirasteh, S., Alesheikh, A.A., Khosravi, K., Panahi, S. and Rezaie, F., 2019. Spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo). Applied Sciences, (Impact Factor: 2.21).

https://doi.org/10.3390/app9183755

17- Lee, S., Panahi, M., Pourghasemi, H.R., Shahabi, H., Alizadeh, M., Shirzadi, A., Khosravi, K., Melesse, A.M., Yekrangnia, M., Rezaie, F. and Moeini, H., 2019. Sevucas: A novel gis-based machine learning software for seismic vulnerability assessment. Applied Sciences, (Impact Factor: 2.21). https://doi.org/10.3390/app9173495

16- Tien Bui, D., Shirzadi, A., Shahabi, H., Chapi, K., Omidavr, E., Pham, B.T., Talebpour Asl, D., Khaledian, H., Pradhan, B., Panahi, M. and Bin Ahmad, B., 2019. A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran). Sensors, (Impact Factor: 2.21). https://doi.org/10.3390/s19112444

15- Chen, W., Panahi, M., Tsangaratos, P., Shahabi, H., Ilia, I., Panahi, S., Li, S., Jaafari, A. and Ahmad, B.B., 2019. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena, (Impact Factor: 5.19).

https://doi.org/10.1016/j.catena.2018.08.025

2018

14- Bui, D.T., Panahi, M., Shahabi, H., Singh, V.P., Shirzadi, A., Chapi, K., Khosravi, K., Chen, W., Panahi, S., Li, S. and Ahmad, B.B., 2018. Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific reports, (Impact Factor: 4.12).

https://doi.org/10.1038/s41598-018-33755-7

13- Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Hoang, N.D., Pham, B.T., Bui, Q.T., Tran, C.T., Panahi, M., Bin Ahmad, B. and Saro, L., 2018. A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides. Remote Sensing, (Impact Factor: 4.18).

https://doi.org/10.3390/rs10101538

12- Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Alizadeh, M., Chen, W., Mohammadi, A., Ahmad, B.B., Panahi, M., Hong, H. and Tian, Y., 2018. Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in cameron highlands, malaysia. Remote Sensing, (Impact Factor: 4.18).

https://doi.org/10.3390/rs10101527

11- Alizadeh, M., Alizadeh, E., Asadollahpour Kotenaee, S., Shahabi, H., Beiranvand Pour, A., Panahi, M., Bin Ahmad, B. and Saro, L., 2018. Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran. Sustainability, (Impact Factor: 2.59). https://doi.org/10.3390/su10103376

10- Khosravi, K., Panahi, M. and Tien Bui, D., 2018. Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrology & Earth System Sciences, (Impact Factor: 4.95).

https://doi.org/10.5194/hess-22-4771-2018

9- Tien Bui, D., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V.P., Chapi, K., Shirzadi, A., Panahi, S., Chen, W. and Bin Ahmad, B., 2018. New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water, (Impact Factor: 2.57).

https://doi.org/10.3390/w10091210

8- Chen, W., Li, H., Hou, E., Wang, S., Wang, G., Panahi, M., Li, T., Peng, T., Guo, C., Niu, C. and Xiao, L., 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of The Total Environment, (Impact Factor: 7.98). https://doi.org/10.1016/j.scitotenv.2018.04.055

7- Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Pradhan, B., Chen, W., Khosravi, K., Panahi, M., Bin Ahmad, B. and Saro, L., 2018. Land subsidence susceptibility mapping in south korea using machine learning algorithms. Sensors, (Impact Factor: 2.21).

https://www.mdpi.com/1424-8220/18/8/2464

6- Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A.X., Chen, W., Kougias, I. and Kazakis, N., 2018. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of The Total Environment, (Impact Factor: 7.98). https://doi.org/10.1016/j.scitotenv.2017.10.114

2017

5- Chen, W., Pourghasemi, H.R., Panahi, M., Kornejady, A., Wang, J., Xie, X. and Cao, S., 2017. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency  ratio,  generalized  additive  model,  and  support  vector  machine techniques. Geomorphology, (Impact Factor: 5.58).

https://doi.org/10.1016/j.geomorph.2017.09.007

4- Chen, W., Panahi, M. and Pourghasemi, H.R., 2017. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena, (Impact Factor: 5.19).

https://doi.org/10.1016/j.catena.2017.05.034

3- Aghdam, I.N., Pradhan, B. and Panahi, M., 2017. Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environmental Earth Sciences, (Impact Factor: 1.76). https://doi.org/10.1007/s12665-017-6558-0

2015

2- Rezaie, F. and Panahi, M., 2015. GIS modeling of seismic vulnerability of residential fabrics considering geotechnical, structural, social and physical distance indicators in Tehran using multi-criteria decision-making techniques. Natural Hazards & Earth System Sciences, (Impact Factor: 2.88).

https://www.nat-hazards-earth-syst-sci.net/15/461/2015/

1- Panahi, M., Rezaie, F. and Meshkani, S.A., 2014. Seismic vulnerability assessment of school buildings in Tehran city based on AHP and GIS. Natural Hazards & Earth System Sciences, (Impact Factor: 2.88). www.nat-hazards-earth-syst-sci.net/14/969/2014/

JOURNAL REVIWER

1. Remote Sensing of Environment (13.58)
2.  Journal of Cleaner Production (IF: 9.29)
3.  Process Safety and Environmental Protection (IF: 7.87)
4. Future Generation Computer Systems (IF: 7.17)
5. Automation in Construction (IF: 7.70)
6. Science of The Total Environment (IF: 7.96)
7. Future Generation Computer Systems (IF: 7.18)
8. Journal of Ambient Intelligence and Humanized Computing (IF: 7.014)
9. Expert Systems with Applications (IF: 6.954)
10. Journal of Environmental Management (IF: 6.78)

11. Applied Soft Computing (IF: 6.72)
12. Geoscience Frontiers (IF: 6.85)
13. Observation and Geoinformation (IF: 5.98)
14. Journal of Hydrology (IF: 5.72)
15. Ecological Informatics (IF:5.8)
16. Weather and Climate Extremes (IF: 5.33)
17. CATENA (IF: 5.19)
18. Natural hazards and earth system sciences (IF: 4.5)
19. Journal of Computational Design and Engineering (IF: 5.2)
20. Natural resources research (IF: 5.146)
21. Journal of Hydrology: Regional Studies (IF: 5.01)
22. Geocarto International (IF: 4.889)
23. Environmental Impact Assessment Review (IF: 4.549)
24. Atmospheric Pollution Research (IF: 4.42)
25. Agricultural Water Management (IF: 4.516)
26. Advances in Water Resources (IF: 4.5)
27. International Journal of Environmental Health Research (IF: 4.459)
28. International Journal of Disaster Risk Reduction (IF: 4.32)
29. Scientific Reports (IF: 4.39)
30. Applied geography (IF: 4.24)
31. Geomorphology (IF: 4.139)
32. Remote Sensing (IF: 4.18)
33. IEEE Access (IF: 4.09)
34. IEEE Geoscience and Remote Sensing Letters (IF :3.89)
35. Geomorphology (IF: 4.13)
36. Process Biochemistry (IF: 3.75)
37. Environmental Processes (IF: 3.7)
38. Geomatics, Natural Hazards and Risk (IF: 3.528)
39. ​​​​​​​Water resources management (IF: 3.517)
40. Geoscience Data Journal (IF:3.5)
41. Heliyon (IF:3.4)
42. Earth Surface Processes and Landforms (IF: 3.3)
43. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IF :3.39)
44. PLOS One (IF: 3.27)
45. Journal of Integrative Medicine (IF: 3.034)
46. Soil Use and Management (IF: 2.9)
47. Earth Science Informatics (IF: 2.87)
48. SN Applied Sciences (IF: 2.8)
49. Urban Water Journal (IF: 2.77)
50. Scientific African (IF: 2.7)
51. International Journal of Green Energy (IF: 2.459)
52. Complexity (IF: 2.59)
53. Hydrological Sciences Journal (IF: 2.559)
54. sustainability (IF: 2.57)
55. Water (IF: 2.52)
56. Cognitive Computation and Systems (IF: 2.5)
57. IET Optoelectronics (IF:2.5)
58. Acta Geophysica (IF: 2.293)
59. Forecasting (IF:2.3)
60. Connection Science (IF: 2.352)
61. Advances in Space Research (IF: 2.152)
62. Natural Hazards (IF: 1.91)
63. Open Geosciences (IF: 1.95)
64. ISPRS International Journal of Geo-Information (IF: 1.72)
65. Journal of Radiation Research and Applied Sciences (IF:1.70)
66. Regional Studies in Marine Science (IF:1.62)
67. Concurrency and Computation: Practice and Experience (IF: 1.36)
68. Concurrency and Computation: Practice and Experience (IF: 1.36)
69. Journal of Current Science and Technology
70. Acta Ecologica Sinica
71. Environmental Challenges
72. Results in Earth Sciences

73. Societal Impacts

CONTRIBUTIONS IN BOOKS

Panahi, M ., Yekrangnia, M., Rezaie, F., Damavandi,A., Panahi, S., Pourghasemi, H.R.., GIS-based determination of suitability of existing schools and new schools site selection using step-wise weight assessment ratio analysis (SWARA), radial basis function (RBF), and imperialist competitive (ICA) algorithms, Spatial Modeling in GIS and R for Earth and Environmental Science, Elsevier, 2018.

Rezaie, F. and Panahi, M, Malaeke, A,. Geosciences and Mining Road Map of Charmahal and Bakhtiari Province., Geological Survey & Mineral Exploration of Iran publication, 2015. (in Persian)

Panahi, M., Rezaie, F.,Malaeke, A,. Geosciences and Mining Road Map of Mazandaran Province., Geological Survey & Mineral Exploration of Iran publication, 2015. (in Persian)

CONFERENCE ARTICLE

Afrasiabi, S., Panahi, M., Rezaie, F., The Relationship between NDVI and LST in the urban area of Mashhad, International Conference on Civil Engineering Architecture & Urban Sustainable Development, 18-19 December of 2013 , Tabriz , Iran. Poster Presentation.

Rezaie, F,. Panahi, M., Mohammadi, A., Site selection suitable locations for temporary housing in Tehran, Iran considering three scenarios of earthquake in northern Tehran, Mosha and Ray faults, , International Conference on Civil Engineering Architecture & Urban Sustainable Development, 27&28 November of 2013, Tabriz , Iran. Poster Presentation. (In Persian)

Ebrahimi, A., Alesheykh, A., Fallah, M., Panahi, M., Designing and Implementation of System for Registration of Mineral Limits of the Country, The 20th National Conference and Exhibition on Geomatics, 29-30 April of 2013, Tehran, Iran. Poster Presentation. (In Persian)

Panahi, M., Rezaie, F., Damavandi, A., Localization of Disaster Points of Tehran city with Using Multivariable Decision Making Method and GIS, The 20th National Conference and Exhibition on Geomatics, 29-30 April of 2013, Tehran, Iran. Poster Presentation. (In Persian)

Damavandi, A., Panahi, M., Rezaie, F., Shirazi, M., Flood Hazard Zonation with using Method of Multivariable Analysis and Geography Information System (case study on Sele Bon Watershed Dist. of Firoz Kouh), The 18th National Conference and Exhibition on Geomatics and International Conference of ISPRS WGII/4,7, 8-9 May of 2011, Tehran, Iran. Poster Presentation. (In Persian).

TEACHING EXPERIENCE

Korea Institute of Geoscience and Mineral Resources (KIGAM), Seoul, South Korea, 2019-2022

• GIS (ARCGIS, ILWIS), Remote Sensing (ENVI, IDRISI)
• AI Programming for Geoscience (e.g., MCDM, Fuzzy Logic, Neural Network and deep learning By Python and MATLAB Language) and Statistical (Python and MATLAB Language).

PROFESSIONAL EXPERIENCE

Korea Institute of Geoscience and Mineral Resources (KIGAM). (Employ Full Time) 06/2019 – 08/2022

• Geoscience data center researcher.

Pars Gostaran Aghigh Engineering Company, Tehran, Iran. (Employ Full Time) 09/2008 – 01/2012

• Expert of natural hazard and disaster management.

COMPUTER SKILLS

• Expertise in Geographic Information Systems (GIS) and Remote Sensing (RS):
• Proficient in the use of software packages such as ArcGIS, SNAP, and ENVI.
• Programming Skills:
• Skilled in programming languages and platforms including Python, R, VB.NET, MATLAB, ArcEngine, and ArcObjects.
• Advanced Data Analysis Techniques:
• Experienced in utilizing innovative methods for data analysis, including: MCDM, Fuzzy Logic, AI models, optimization algorithms, and image processing.
• Good knowledge of Fracture Network Modeling, Alpha Shape, Fry Modeling

RESEARCH INTEREST

Monitoring natural hazards, environmental changes, and social hazards using GIS and remote sensing through advanced analytical tools, including:

• • Machine learning
  • Mathematical algorithms
  • Spatial analysis (e.g., fractal geometry, alpha shape, graph theory, spatial and topological analyses)
  • Statistical analysis (using statistical software and programming)