Advance in Time Series Modelling for Water Resources Management

Dear Colleagues,

Water resources are at the core of sustainable socio-economic development and environmental protection for future generations. Most of the current methods in water resource management are based on time series modelling, which assumes linearity in water demand and water use data, and utilises models and methods that do not consider the complex nature of the datasets involved. Accurate forecasting of water quantity/quality time series has major economic, social, and environmental implications for sustainable development. Analysis of the historic dataset-based time-series using advanced artificial intelligence modelling techniques offers promising new water resources management tools for overcoming the limitations of using the complex input datasets of the deterministic hydrologic models.

This Special Issue will focus on two primary goals: (1) Developing innovative artificial intelligence (AI) and/or stochastic-based techniques for water quantity/quality time series modelling purposes and (2) establishing more accurate and efficient predictive models for the monitoring and real-time prediction, optimisation, and for the automation of the meteorological and hydrological watershed variables. These objectives will also enhance our understanding of water resource problems associated with sustainable development in today’s rapidly globalizing and urbanising world. Research studies focusing on complex and dynamic meteorological/hydrological watershed variables and implementing novel modelling approaches, developing new tools, or improving the existing predictive models are especially welcome.

Prof. Hossein Bonakdari
Prof. Amir Hossein Azimi
Prof. Bahram Gharabaghi
Dr. Andrew D Binns
Dr. Pijush Samui
Guest Editors

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