A Case Study on Long-Term Water Consumption Forecasting in Türkiye By Using The Hodrick–Prescott Filter and Artificial Neural Networks
DOI:
https://doi.org/10.63556/tisej.2025.1693Keywords:
Water Consumption Forecasting, Hodrick–Prescott Filtering, Artificial Neural Networks, Multiple Linear Regression, TürkiyeAbstract
Consumption planning for scarce resources is one of the most critical activities for meeting the unlimited needs and demands of humankind. In this context, the management of existing water resources and their efficient use play a vital role in achieving countries’ sustainable development goals. However, water resources are becoming increasingly fragile due to their limited and uneven distribution, anthropogenic pollution, and the impacts of climate change, which makes effective water management necessary. Accurate short and long-term water demand forecasting is essential to ensure reliable and cost-efficient operation of water distribution systems. This study aims to forecast Türkiye’s long-term water consumption by analyzing the factors influencing water use between 2001 and 2021 using the Hodrick–Prescott (HP) filtering method and Artificial Neural Networks (ANNs). The objective is to generate water consumption forecasts for the years 2030 and 2050. In this context, HP filtering is first employed to remove short-term fluctuations and extract the trend and cyclical components of the data. These components are then modeled by using ANN and Multiple Linear Regression (MLR) methods. According to the findings, while no significant increase in water consumption is projected for 2030, a substantial rise is expected by 2050. The 2030 forecasts indicate that policy interventions targeting water consumption, aligned with the Sustainable Development Goals, will be effective. Overall, the results suggest the need to develop new water resources, enhance wastewater recycling, and strengthen water management policies to balance future water demand with supply.
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