Rana Muhammad Adnan Ikram

About

Dr. Rana Muhammad Adnan has been working as a Researcher at Hohai University. He is a passionate researcher in the fields of water resources, atmospheric sciences, and applied soft computing. His research fields are developing novel algorithms and advanced computing methods towards innovative hydrologic and atmospheric variables modeling solutions. The scope of his research is quite broad, covering water resources engineering, environmental engineering, knowledge-based system development, and the implementation of data analytic and artificial intelligence models. His main research interests include climate variability and changes, climate change impacts on hydrological processes and water resources, water resource management, and natural hazards, such as vulnerability and risk assessment with the aid of advanced AI. He has published over 100 research articles within international journals and has total citations of over 3500 (Google Scholar H-Index = 35). He has collaborated with over 30 international countries and more than 120 researchers. He has served as a reviewer for more than 50 international journals. He has also served as a Guest Editor for Special Issues. He ranks among the top 2% of global scientists in 2023. In addition, he hosted more than ten key research projects, including NSFC and Guangdong Natural Science Provincial Project.

Employment

• Hohai University Researcher
  2018 - Present
• Muhammad Nawaz Shareef University of Agriculture Assitant Professor
  2017 - 2018

Education

• Huazhong University of Science and Technology  PhD in Hydraulic Engineering
  2013 - 2017
• University of Engineering and Technology  M.Sc in water resources engineering
  2010 - 2013
• University of Agriculture Faisalabad  B.Sc (Engg) in Agriculture Engineering
  2006 - 2010

Projects & Funding

Projects & funding information is unavailable.

Publications (124)

• Improving groundwater level prediction based on optimized deep learning model with Coati algorithm
  Environmental Modelling & Software 2026 DOI: 10.1016/j.envsoft.2026.107071
  +Save
• Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis
  Scientific Reports 2026 DOI: 10.1038/s41598-026-38757-4
  +Save
• Local Climate Zone-Conditioned Generative Modelling of Urban Morphology for Climate-Aware and Water-Relevant Planning in Coastal Megacities
  Water 2026 DOI: 10.3390/w18030312
  +Save
• SWOT-Based Evaluation of Nature-Based Solutions for Stormwater Resilience in Historic Urban Landscapes
  Water 2025 DOI: 10.3390/w17213084
  +Save
• Advancing Nature-Based Solutions with Artificial Intelligence: A Bibliometric and Semantic Analysis Using ChatGPT
  Atmosphere 2025 DOI: 10.3390/atmos16091102
  +Save
• Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP)
  Earth Science Informatics 2025 DOI: 10.1007/s12145-025-01796-y
  +Save
• Decentralized Coupled Grey–Green Infrastructure for Resilient and Cost-Effective Stormwater Management in a Historic Chinese District
  Water 2025 DOI: 10.3390/w17152325
  +Save
• Strategic Planning for Nature-Based Solutions in Heritage Cities: Enhancing Urban Water Sustainability
  Water 2025 DOI: 10.3390/w17142110
  +Save
• Forecasting maximal and minimal air temperatures using explainable machine learning: Shapley additive explanation versus local interpretable model-agnostic explanations
  Stochastic Environmental Research and Risk Assessment 2025 DOI: 10.1007/s00477-025-02984-4
  +Save
• Assessing Groundwater Potential in the Kabul River Basin of Pakistan: A GIS and Analytical Hierarchy Process Approach for Sustainable Water Management
  Water 2025 DOI: 10.3390/w17111584
  +Save