Water Consumption Prediction Based on Improved Fractional-Order Reverse Accumulation Grey Prediction Model

Yuntao Zhu, Binglin Zhang () and Jun Li
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Yuntao Zhu: School of Ecology, Hainan University, Haikou 570228, China
Binglin Zhang: College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Jun Li: School of Ecology, Hainan University, Haikou 570228, China

Sustainability, 2025, vol. 17, issue 21, 1-25

Abstract: Predicting urban water consumption helps managers allocate, reserve, and schedule water resources in advance, avoiding supply–demand imbalances. In practical terms, the improved forecasting model can assist urban water managers in planning supply schedules, optimizing reservoir operations, and allocating resources efficiently, thereby supporting sustainable water management in rapidly developing tropical island tourist cities. Traditional forecasting models typically assume that the statistical properties of the data remain stable, an assumption often violated under changing environmental conditions. In addition, tropical island tourist cities have unique hydrological characteristics and frequently fluctuating tourist populations, making water consumption forecasting even more complex in these settings. To address the aforementioned problems, this study develops an improved fractional-order reverse accumulation grey model. Based on the principle of new information priority, the weighted processing of historical data enhances the model’s learning capability for new data. The optimal fractional order is determined using the Greater Cane Rat Algorithm, and the optimized fractional-order reverse accumulation grey model is then applied to forecast water consumption in Sanya City. The results demonstrate that the proposed model achieves a relative error of 4.28% for Sanya’s water consumption forecast, outperforming the traditional grey model (relative error 5.24%), the equally weighted fractional-order reverse accumulation model (relative error 4.37%), and the ARIMA model (relative error 6.92%). The Diebold–Mariano (DM) test further confirmed the statistically significant superiority of the proposed model over the traditional model.

Keywords: fractional-order; grey prediction model; new information priority; greater cane rat algorithm; water consumption prediction; Sanya city (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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