Mr. Mahdi Mohammadnezhad | Water Resource | Best Researcher Award

Mr. Mahdi Mohammadnezhad | Water Resource | Best Researcher Award

Mr. Mahdi Mohammadnezhad | Water Resource | Ferdowsi University of Mashhad | Iran

Mr. Mahdi Mohammadnezhad is an emerging researcher in the field of water resources, hydrological modeling, and sensing technologies whose academic and professional journey reflects a strong commitment to advancing sustainable water management and integrating modern data-driven methods into traditional water balance frameworks; he pursued his Bachelor of Science in Water Science and Engineering at Ferdowsi University of Mashhad, where he laid the foundation for his expertise, and later completed. his Master of Science in Water Science and Engineering with specialization in water resources at the same institution, ranking first in his class and producing a thesis focused on enhancing the QDWB balance model through optimization and the Budyko framework, which exemplifies his ability to combine hydrological theory with practical innovation; during his academic training. he also gained extensive professional experience as a research assistant at Ferdowsi University of Mashhad, contributing to projects involving water-sensitive city planning, irrigation basin studies, and crop yield prediction under environmental stress, while his role as a senior technical expert at Hydrotech Toos Consulting Engineers enabled him to apply his scientific knowledge to real-world water management challenges

Professional Profile: Google scholar

Selected Publications

  1. Enhancing QDWB Balance Model by Utilizing Optimization and Implementation of Budyko Framework – 2024 – Cited by 12

  2. Estimation of saffron yield using machine learning and remote sensing techniques in the face of climate change – 2024 – Cited by 8

  3. A Novel Hybrid Model for Actual Evapotranspiration Estimation in Data-Scarce Arid Regions: Integrating Modified Budyko and Machine Learning Models using Deep Learning – 2024

  4. Assessment of the climate change scenarios on the CWatM model on a monthly and daily basis using an evolutionary algorithm – 2024