A dedicated data scientist with expertise in applied AI (deep and machine learning) techniques for air quality, bias correction, hydrology, water resources, and climate extremes. I have a decade of experience in building robust Machine Learning solutions, combining professional and research experience to gain deep knowledge in hydrology and water resources. I am passionate about leveraging data science and AI to drive impactful data-driven decisions.
Publication profile
Scopus
Scholar
š Evaluation for Best Researcher Award
š Streng ths for the Award:
Extensive Research Experience: With over a decade of experience in applied AI and machine learning for hydrology, climate extremes, and air quality, Dr. Masrur Ahmed has developed a solid research foundation, making significant contributions to environmental and climate sciences. Prolific Publications: A commendable h-index of 16 and an i10-index of 25, along with a considerable number of publications in high-impact journals, reflects Dr. Ahmed’s active research engagement and scholarly impact. Innovative Application of AI: His work in developing hybrid deep learning models for hydrological prediction and air quality forecasting is cutting-edge, pushing the boundaries of traditional environmental science research. Recognized Expertise: His role as a reviewer and guest editor in respected journals, along with memberships in several professional organizations, underscores his standing in the scientific community. Global Collaboration: His involvement in international projects, such as those with the University of Melbourne, CSIRO, and BOM, indicates his ability to work effectively across disciplines and borders.
š Areas for Improvement:
Interdisciplinary Applications: While Dr. Ahmed excels in his niche, expanding his AI applications to other fields, such as agriculture or public health, could further enhance his research portfolio. Leadership Roles: Although he has served as Head of the Civil Engineering Department, more leadership roles in larger, interdisciplinary research projects could solidify his candidacy for top awards. Public Engagement: Increasing efforts to communicate his research to a broader audience through public talks, popular science articles, or media could enhance the societal impact of his work.
š EDUCATION
2019 ā 2022 PhD in Applied Artificial Intelligence University of Southern Queensland, Australia Thesis: Development of Deep Learning Hybrid Models for Hydrological Prediction. 2011 ā 2017 MSc in Civil & Environmental Engineering Shahjalal University of Science & Technology
Thesis: Development of ANN Model to Predict the DO Concentration of the Surma River. 2005 ā 2009 BSc in Civil & Environmental Engineering
Shahjalal University of Science & Technology
š¼ WORK EXPERIENCE
Scientist, Climate and Atmospheric Science Department of Climate Change, Energy, the Environment and Water JAN/2024 ā Present Developing machine learning and deep learning models focused on emission modeling and air quality forecasting. Research Fellow in Water Resource Modelling The University of Melbourne, Australia FEB/2022 ā JAN/2024 Conducted research in water demand forecasting under global warming scenarios, developed probabilistic deep learning models, and collaborated with leading institutions like CSIRO and BOM. Research Scholar
University of Southern Queensland (UniSQ), Australia FEB/2019 ā FEB/2022 Developed hybrid deep learning algorithms for hydrological forecasting in the Australian Murray Darling Basin.
š§Ŗ RESEARCH HIGHLIGHTS
- ORCiD: 0000-0002-7941-3902
- Google Scholar: Link
- Citations: 1048
- h-index: 16
- i10-index: 25
- Review/Guest Editor:
- Frontiers in Artificial Intelligence
- Journal of Land (Special Edition)
Publication top notes
- Title: Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River
Authors: AAM Ahmed, SMA Shah
Year: 2017
- Title: Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs)
Authors: AAM Ahmed
Year: 2014
- Title: Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
Authors: AAM Ahmed, RC Deo, Q Feng, A Ghahramani, N Raj, Z Yin, L Yang
Year: 2021
- Title: LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios
Authors: AAM Ahmed, RC Deo, A Ghahramani, N Raj, Q Feng, Z Yin, L Yang
Year: 2021
- Title: Deep learning forecasts of soil moisture: convolutional neural network and gated recurrent unit models coupled with satellite-derived MODIS, observations and synoptic-scale variables
Authors: AAM Ahmed, RC Deo, N Raj, A Ghahramani, Q Feng, Z Yin, L Yang
Year: 2021
- Title: A Canadian water quality guideline-water quality index (CCME-WQI) based assessment study of water quality in Surma River
Authors: GM Munna, MMI Chowdhury, AAM Ahmed, S Chowdhury, MM Alom
Year: 2013
- Title: Hybrid deep learning method for a week-ahead evapotranspiration forecasting
Authors: AAM Ahmed, RC Deo, Q Feng, A Ghahramani, N Raj, Z Yin, L Yang
Year: 2022
- Title: New double decomposition deep learning methods for river water level forecasting
Authors: AAM Ahmed, RC Deo, A Ghahramani, Q Feng, N Raj, Z Yin, L Yang
Year: 2022
- Title: Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm
Authors: MA Haque, B Chen, A Kashem, T Qureshi, AAM Ahmed
Year: 2023
š
Conclusion:
Dr. Masrur Ahmed is a strong contender for the Best Researcher Award, given his significant contributions to applied AI in environmental sciences, extensive publication record, and active role in the research community. To further strengthen his candidacy, broadening the interdisciplinary application of his work and increasing his public engagement could be beneficial.