Advanced computational methodologies for environmental modeling and sustainable water management

Guest Editors:
Ozgur Kisi: University of Applied Sciences Lübeck, Germany
Rana Muhammad Adnan: Hohai University, China

Submission Status: Closed | Submission Deadline: Closed

This Collection no longer accepts submissions.

Environmental Sciences Europe is calling for submissions to our Collection on Advanced computational methodologies for environmental modeling and sustainable water management.

This special issue is related to the sustainable management of water resources and the environment.

Image Credit: RomoloTavani / Getty Images / iStock

This Collection supports and amplifies research related to SDG 15.

Meet the Guest Editors

Ozgur Kisi: University of Applied Sciences Lübeck, Germany

Dr. Kisi is a full professor in Ilia State Unievrsity and has been working as a guest professor at University of Applied Sciences, Lübeck, Germany. He is serving as an Editorial Board Member of several reputed journals (e.g. ASCE Journal of Hydrologic Engineering, Hydrology Research, Hydrological Sciences Journal, Sustainability and Arabian Journal of Geosciences). He has authored more than 500 research articles, 15 chapters and 30 discussions. Dr. Kisi is the recipient of the 2006 International Tison Award (given by the International Association of Hydrological Sciences (IAHS). In 2022, Dr. Kisi had the 5th and 6th ranks in the fields of Environmental Engineering and Meteorology & Atmospheric Sciences in the world according to the database (list of top 2% scientists in the world) developed by the researchers from Stanford University. He was selected as the highly cited researcher by the Clarivate in 2021. He is a principle member of Turkish Academy of Science (selected in 2012).]

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Rana Muhammad Adnan: Hohai University, China
Dr. Rana Muhammad Adnan has been working as a Researcher at Hohai University. He is a passionate researcher in the field of water resources, atmospheric sciences, and applied soft computing. His research fields are developing novel algorithms and advanced computing methods towards the innovative solution of hydrologic and atmospheric variables modeling. 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 Resources Management, Natural Hazards vulnerability and risk. He has published over 50 research articles within international journals and total number of citations over 1500 (Google Scholar H-Index = 25). He has collaborated with over 25 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.

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About the collection

For a sustainable environment and ecosystem conservation, precise monitoring and modeling of environmental variables are necessary. Urbanization, shortage of water, and industrialization adversely affects the natural balance of environmental variables. Therefore, these alterations in environmental variables not only affects climate change but also seriously affects the health of human, animal and aquatic life. With the objective of a sustainable environment and ecosystem, environmental and water resources experts can avoid the negative impacts of changes in environmental and water resources variables. However, accurately assessment of these changes is a challenging task due to many external climatic, geological, and human factors. Therefore, to precisely monitor and model the changes in environmental variables, robust and advanced computing methods are required. The main objective of this special issue is to find better smart solutions for the sustainable management of water and environmental resources using advanced computing methods. This issue welcomes contributions to innovative approaches in the field of a sustainable environment.

• Advanced machine learning applications
• Application of deep learning approaches
• Modeling environmental variables
• Monitoring environmental parameters
• Environmental assessment
• Modeling hydrological variables
• Air quality modeling
• Water quality modeling
• Groundwater level modeling
• Applications of GIS-based and satellite-based data
• Temporal and spatial environmental modeling

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