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Call for Papers: Special Collection on Harnessing Nature-Based Solutions and Machine Learning for Sustainable Water Resource Planning and Management
Guest Editors:
Dr. Shray Pathak, Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India, ([email protected])
Prof. Vijay P. Singh, Distinguished Professor, Department of Biological and Agricultural Engineering, and Zachry Department of Civil & Environmental Engineering, Texas A&M University, Texas 77843, U.S.A., ([email protected])
Prof. Dr. Enrico Creaco, Full Professor, Department of Civil Engineering and Architecture, Università degli Studi di Pavia, Via Ferrata 3, 27100 Pavia, Italy,([email protected])
Prof. Dr. Andrea Cominola, Assistant Professor, Chair of Smart Water Networks, Technische Universität Berlin and Einstein Center Digital Future, Straße des 17. Juni 135, D- 10623 Berlin, Germany, ([email protected])
Aims & Scope
Nature-based solutions (NBS) for sustainable water management harness natural processes and ecosystems to address various water-related challenges, offering a harmonious blend of environmental, social, and economic benefits. These solutions include restoring wetlands to filter pollutants, conserving watersheds that regulate water flow and quality, and implementing blue and green infrastructure, such as rain gardens and green roofs, to manage stormwater and reduce urban flooding. By mimicking nature, NBS enhance biodiversity, support ecosystem services, and increase resilience to hydroclimatic extreme events. NBS provide a cost-effective alternative to traditional engineering approaches, often requiring maintenance and operational costs. Furthermore, by including NBS into urban and rural planning, communities can improve water security, promote sustainable agriculture, and enhance public health, ultimately contributing to the resilience and sustainability of water resources.
This special collection underscores the promise of utilizing machine learning (ML) algorithms to integrate NBS in planning and managing water resources. Leveraging ML techniques in planning, design, operation, monitoring, maintenance, and long-term management of NBS offers a transformative framework for addressing pressing water resource challenges. Through data analysis and predictive modelling, ML algorithms enable informed decision-making, optimized resource allocation, and adaptive management strategies. This synergy enhances the efficiency and effectiveness of NBS implementation, promoting ecosystem resilience, biodiversity conservation, and long-term sustainability. Moreover, the integration of interdisciplinary knowledge fosters collaboration between water resource experts, data scientists, and ecologists, facilitating holistic solutions to complex water management issues. By promoting the adoption of nature-based approaches and harnessing the power of ML, this paves the way for more resilient and sustainable water systems, contributing to global efforts towards environmental stewardship and resilience in the face of climate change. The special collection highlights the synergistic relationship between ML and NBS in addressing complex water resource challenges, while advocating for holistic and adaptive approaches towards sustainable water management.
Specific topics of interest include, but are not limited to:
- Leveraging ML algorithms to analyze large datasets and learn insights for sustainable water resources planning through NBS.
- ML-based predictive models to forecast water availability, demand, and quality for effective resource allocation and NBS prioritization.
- ML algorithms for analyzing vast amounts of data to optimize design and implementation of NBS.
- ML techniques, including spatial analysis, image processing, and sensor-based analysis for identifying suitable/optimal locations for nature-based interventions, and assess their effectiveness over time.
- ML-based approaches for accounting uncertainties and changing environmental conditions affecting NBS performance over time.
- Approaches and frameworks to facilitate participatory decision-making and community engagement through integration of ML and NBS.
- Encouraging cross-field multidisciplinary research, particularly between the physical/natural sciences and social sciences/arts and humanities, to promote at all levels the uptake of NBSs.
- Pursuing the identification of the systemic implications of NBS, in terms of quantitative evaluation of their benefits, services, potential risks, and unintended consequences, to win over the skepticism and negative perceptions around NBS implementation.
Submission Deadlines
- Open Call for Paper: June 15, 2024
- Close Call for Papers: May 31, 2025
- Target to Complete: July 31, 2025
Submission Guidelines
- Please submit your manuscript via the ASCE Journal of Water Resources Planning and Management website.
- Once on the Editorial Manager website, please indicate that your paper is for the special collection “Harnessing Nature-Based Solutions and Machine Learning for Sustainable Water Resource Planning and Management”.
- Detailed information on the submission process is provided in the “Publishing in ASCE Journals” section of the ASCE Author Center.
Please note that all accepted papers submitted in response to this Call for Papers will be published in regular issues of the Journal of Water Resources Planning and Management and assembled online on a page dedicated to this Special Collection. See Journal of Water Resources Planning and Management Special Collections for the list of Special Collections already published.