Overview
Mehdi joined the Centre for Resilience in Environment, Water and Waste (CREWW), University of Exeter as a Ph.D. student in May 2023. His PhD research mainly focus on Natural Flood Management Solutions.
Before he starts his PhD in Exeter, Mehdi graduated with a bachelor degree in water engineering in 2015 from Lorestan University, Iran. He also received his M.Sc. in the field of water resources engineering from the University of Tehran, Iran in 2017.
Since finishing his degrees, he has done some research about flooding, flood seasonality, flood risk, water management, groundwater, Participatory groundwater management and land subsidence, published in prestigious scientific journals including STOTEN and cleaner production.
Mehdi has also been a teacher assistant in different departments of the University of Tehran and had some collaborations in applied projects about surface water-groundwater interactions and aquifer storage and recovery management with the Water Institute and Groundwater Research Institute of the University of Tehran as a researcher from April 2015 to March 2023.
Qualifications
MSc Water Resources Engineering- University of Tehran, Iran
BSc Water Engineering - Lorestan University, Iran
Links
Research
Research projects
Optimising Multiple Natural flood Management Solutions
Supervisors: Prof. Richard Brazier, Dr. Diego Panici, Dr. Alan Puttock
Funding Body: DIRP Project
Flooding is one of most damaging phenomena around the globe that affect humans’ life and economy. So, practical solutions should be used in order to decrease its dire consequences. The Natural Flood Management is an approach, called from nature to nature, has been implemented across the world. Although this useful solution has Hydrological (Flood Reduction, Water storage and Water quality), ecological (Infiltration, Biodiversity and Sediment capture) and socio-economical (Sustainable, flexible, Acceptable, Environmental-friendly) advantages, its gaps cannot be neglected.
In this project this team tries to do a comprehensive study so as to address the knowledge gap that surrounds decision-making around NFM solutions, quantifying which approaches are best, where and characterising the differences that each approach might deliver in terms of flood attenuation and water storage. Working across a wide range of field sites, and deploying a Multi-site, Before, After, Control, Impact experimental design, this research will deliver enhanced empirical understanding of Natural Flood Management approaches.
This project involves an exciting interdisciplinary collaboration between experienced researchers at the University of Exeter, the University of Plymouth, Devon County Council, the EA and multiple other project partners.
Broad research specialisms:
His academic interests include Flooding, Hydrology, Flood Seasonality, Water Management, Groundwater, Land Subsidence, River-Groundwater Interactions, Aquifer Storage and Recovery Management and Societal Issues in Water.
Research Group
CREWW
Publications
Key publications | Publications by category | Publications by year
Publications by category
Journal articles
Rashidi Shikhteymour S, Borji M, Bagheri-Gavkosh M, Azimi E, Collins TW (2023). A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods. Applied Geography, 158, 103035-103035.
Bagheri-Gavkosh M, Hosseini SM (2023). Flood Seasonality Analysis in Iran: a Circular Statistics Approach. Journal of Hydrologic Engineering, 28(2).
Valizadeh N, Bagheri-Gavkosh M, Bijani M, Hayati D (2022). Application of social identity models of collective action to facilitate participation in groundwater aquifer storage and recovery management.
Frontiers in Psychology,
13Abstract:
Application of social identity models of collective action to facilitate participation in groundwater aquifer storage and recovery management
Aquifer storage and recovery (ASR) is considered as an innovative method and an alternative one for sustainable management of water resources that has, in recent years, attracted the attention of experts and thinkers. Implementation of this method would entail the participation and collective action of various stakeholders. In this process, farmers are considered as the most important stakeholders; and limited studies have been conducted on their intentions to participate in collective actions of ASR management. In this regard, the investigation of farmers’ intention to participate in ASR and its determinants, using social identity models of collective action, was selected as the main purpose of the present study. For this purpose, using a cross-sectional survey, 330 Iranian farmers were interviewed. In this study, the ability of the dual-pathway model of collective action (DPMCA) and the encapsulation model of social identity in collective action (EMSICA) was evaluated and compared to explain farmers’ intentions towards participation in ASR management. The results revealed that the both models had good predictive powers. However, DPMCA was a stronger framework than EMSICA for facilitating farmers’ collective behaviors in the field of participation in ASR management. This is one of the most important results of the present research that might be used by various users including decision makers, managers, and practitioners of water resources management in Iran and generally the world. Finally, the creation of a “we thinking system” or social identity in the field of ASR management was highlighted as one of the most important take-home messages.
Abstract.
Hosseini FS, Choubin B, Bagheri‐Gavkosh M, Karimi O, Taromideh F, Mako C (2022). Susceptibility Assessment of Groundwater Nitrate Contamination Using an Ensemble Machine Learning Approach.
Groundwater,
61(4), 510-516.
Abstract:
Susceptibility Assessment of Groundwater Nitrate Contamination Using an Ensemble Machine Learning Approach
AbstractGroundwater pollution susceptibility mapping using parsimonious approaches with limited data is of utmost importance for water resource and health planning, especially in data‐scarce regions. Current research assesses groundwater nitrate susceptibility by considering the various combination of explanatory variables. In this study, the novel machine learning models of weighted subspace random forest (WSRF) and generalized additive model using LOESS (GAMLOESS) are applied, and the results are compared with well‐known machine learning models of K‐nearest neighbors (KKNN) and random forest (RF). The optimum combination of inputs for groundwater nitrate susceptibility mapping is identified using the k‐fold cross‐validation methodology. Results indicated that the combination of variables of precipitation, groundwater level, and lithology had the best performance among the 16 combinations. Modeling performance using the optimum combination demonstrated that the new ensemble approach, the WSRF model, had superior performance according to the evaluation metrics of accuracy (0.87), kappa (0.73), precision (0.92), false alarm ratio (0.08), and critical success index (0.75). The susceptibility assessment results of this paper can be a useful tool in developing strategies for the prevention and protection of groundwater pollution.
Abstract.
Bagheri-Gavkosh M, Hosseini SM, Ataie-Ashtiani B, Sohani Y, Ebrahimian H, Morovat F, Ashrafi S (2021). Land subsidence: a global challenge. Science of the Total Environment, 778, 146193-146193.
Parizi E, Bagheri-Gavkosh M, Hosseini SM, Geravand F (2021). Linkage of geographically weighted regression with spatial cluster analyses for regionalization of flood peak discharges drivers: Case studies across Iran. Journal of Cleaner Production, 310, 127526-127526.
Bagheri M, Kholghi M, Hosseini SM, Amiraslani F, Hoorfar A (2020). Participatory approach in Aquifer Storage and Recovery management in Arid zones, does it work?. Groundwater for Sustainable Development, 10, 100368-100368.
Publications by year
2023
Rashidi Shikhteymour S, Borji M, Bagheri-Gavkosh M, Azimi E, Collins TW (2023). A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods. Applied Geography, 158, 103035-103035.
Bagheri-Gavkosh M, Hosseini SM (2023). Flood Seasonality Analysis in Iran: a Circular Statistics Approach. Journal of Hydrologic Engineering, 28(2).
2022
Valizadeh N, Bagheri-Gavkosh M, Bijani M, Hayati D (2022). Application of social identity models of collective action to facilitate participation in groundwater aquifer storage and recovery management.
Frontiers in Psychology,
13Abstract:
Application of social identity models of collective action to facilitate participation in groundwater aquifer storage and recovery management
Aquifer storage and recovery (ASR) is considered as an innovative method and an alternative one for sustainable management of water resources that has, in recent years, attracted the attention of experts and thinkers. Implementation of this method would entail the participation and collective action of various stakeholders. In this process, farmers are considered as the most important stakeholders; and limited studies have been conducted on their intentions to participate in collective actions of ASR management. In this regard, the investigation of farmers’ intention to participate in ASR and its determinants, using social identity models of collective action, was selected as the main purpose of the present study. For this purpose, using a cross-sectional survey, 330 Iranian farmers were interviewed. In this study, the ability of the dual-pathway model of collective action (DPMCA) and the encapsulation model of social identity in collective action (EMSICA) was evaluated and compared to explain farmers’ intentions towards participation in ASR management. The results revealed that the both models had good predictive powers. However, DPMCA was a stronger framework than EMSICA for facilitating farmers’ collective behaviors in the field of participation in ASR management. This is one of the most important results of the present research that might be used by various users including decision makers, managers, and practitioners of water resources management in Iran and generally the world. Finally, the creation of a “we thinking system” or social identity in the field of ASR management was highlighted as one of the most important take-home messages.
Abstract.
Hosseini FS, Choubin B, Bagheri‐Gavkosh M, Karimi O, Taromideh F, Mako C (2022). Susceptibility Assessment of Groundwater Nitrate Contamination Using an Ensemble Machine Learning Approach.
Groundwater,
61(4), 510-516.
Abstract:
Susceptibility Assessment of Groundwater Nitrate Contamination Using an Ensemble Machine Learning Approach
AbstractGroundwater pollution susceptibility mapping using parsimonious approaches with limited data is of utmost importance for water resource and health planning, especially in data‐scarce regions. Current research assesses groundwater nitrate susceptibility by considering the various combination of explanatory variables. In this study, the novel machine learning models of weighted subspace random forest (WSRF) and generalized additive model using LOESS (GAMLOESS) are applied, and the results are compared with well‐known machine learning models of K‐nearest neighbors (KKNN) and random forest (RF). The optimum combination of inputs for groundwater nitrate susceptibility mapping is identified using the k‐fold cross‐validation methodology. Results indicated that the combination of variables of precipitation, groundwater level, and lithology had the best performance among the 16 combinations. Modeling performance using the optimum combination demonstrated that the new ensemble approach, the WSRF model, had superior performance according to the evaluation metrics of accuracy (0.87), kappa (0.73), precision (0.92), false alarm ratio (0.08), and critical success index (0.75). The susceptibility assessment results of this paper can be a useful tool in developing strategies for the prevention and protection of groundwater pollution.
Abstract.
2021
Bagheri-Gavkosh M, Hosseini SM, Ataie-Ashtiani B, Sohani Y, Ebrahimian H, Morovat F, Ashrafi S (2021). Land subsidence: a global challenge. Science of the Total Environment, 778, 146193-146193.
Parizi E, Bagheri-Gavkosh M, Hosseini SM, Geravand F (2021). Linkage of geographically weighted regression with spatial cluster analyses for regionalization of flood peak discharges drivers: Case studies across Iran. Journal of Cleaner Production, 310, 127526-127526.
2020
Bagheri M, Kholghi M, Hosseini SM, Amiraslani F, Hoorfar A (2020). Participatory approach in Aquifer Storage and Recovery management in Arid zones, does it work?. Groundwater for Sustainable Development, 10, 100368-100368.
Mehdi_Bagheri_Gavkosh Details from cache as at 2023-09-26 08:04:40
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Teaching
Mehdi has helped different faculties of University of Tehran as a Postgraduate Teaching Associate on the following modules:
- Enquiring Hydrology, Department of Irrigation and Remediation, University of Tehran (January 2021 – June 2021)
- Groundwater, Department of Irrigation and Remediation, University of Tehran (January 2020 – June 2020)
- Water Resource Management, Department of RS & GIS, University of Tehran (January 2019 – June 2019)
- Hydrology, Department of Physical Geography, University of Tehran (January 2018 – June 2018)
