The Forecasting Models for Amount of Water in Nam Un Dam, Sakon Nakhon Province
Article Sidebar
Main Article Content
Abstract
This study aimed to create forecasting models for the daily, weekly, and monthly water volumes of the Nam Un dam, Sakon Nakhon Province, using multiple regression analysis and the four-time series analysis techniques. The data consist of the water volume (in million cubic meters), water surface area, drainage volume, and related meteorological variables recorded by the Lam Num Un Irrigation Project from January 1, 2017, to November 30, 2021. To create the forecast models, the data were divided into two parts; the last 12 periods of data were used to compare the performance of the model, while the remaining data were used to create the model. The model’s efficiency was assessed based on the lowest MAPE criteria. The results showed that the Holt and Winter’s multiplicative method was the most accurate daily water volume forecasting model for the Nam Un dam, followed by the three-point simple moving average method. For weekly and monthly water volume forecasting models, the regression model had the lowest MAPE, followed by the three-point simple moving average method.
Article Details
References
Office of the National Water Resources, 2019,. The 20-year Water Resource Management Master Plan (2018-2037), Thailand: Bangkok (in Thai)
Sangkarak, S., Rattanaphan, P., Phetrak, A., and Kittipongvises, S., 2020, Impacts of climate change on water resources and management, Environmental Journal, 24:(1), pp.1-8. (in Thai)
Impithuk, W., 2017, The Great Flood Crisis in Sakon Nakhon 2017 and Opportunities for Sustainable Development Available Source: https://www.csc.ku.ac.th/th/wp-content/ uploads/2017/08/, March 26, 2022. (in Thai)
Kanthananon, K., 2018, Statistical Forecasting, 1st ed., Bangkok: Se-Education Public Company Limited. (in Thai)
Tongsiri, J. and Kangrang, A., 2018, Prediction of Future Inflow under Hydrological Variation Characteristics and Improvement of Nam Oon Reservoir Rule Curve using Genetic Algorithms, Journal of Science and Technology Mahasarakham University. 37(6): 775-788. (in Thai)
Khai, W. J., Alraih, M., Ahmed, A. N., Fai, C. M., EL-SHAFIE A. and EL-SHAFIE, A., 2019, Daily Forecasting of Dam Waterlevels using Machine Learning, International Journal of Civil Engineering and Technology (IJCIET), 10(6): 314-323.
Fongngen, W., Arreerard, W. and Phomasakha Na Sakolnakorn, P., 2018, Forecasting Daily Discharge in Kievlom Dam Using Data Mining Techniques, Journal of Modern Management Science, 10(2): 121-131. (in Thai)
Pengsiri, P., Sodsee, S. and Meesad. P., 2018, A Comparison of Optimal Drainage Methods based on Time Series Forecasting Technique, Journal of Science and Technology Mahasarakham University, 37(5):715-725. (in Thai)
Castillo-Botón, C., Casillas-Pérez, D., Casanova-Mateo, C., Moreno-Saavedra, L. M., Morales-Díaz, B., Sanz-Justo, J., Gutiérrez, P. A. and Salcedo-Sanz, S., 2020, Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques, Journal of Water, Basel, Switzerland. 12: 1-23.
Wongoutong, C., 2021, The Effect on Forecasting Accuracy of the Holt-Winters Method When Using the Incorrect Model on a Non-Stationary Time Series, Thailand Statistician, 19(3): 565-582.
Anuruddhika, M.L.P., Premarathna, L.P.N.D., Perera, K.K.K.R., Hansameenu, W.P.T., and Weerasinghe., V.P.A., 2021, The Holt-Winters’ method for forecasting water discharge, in Attanagalu Oya in International Conference on Applied and Pure Sciences, 2021, University of Kelaniya, Sri Lanka. pp. 49-55.
Mgandu, F. A., Mkandawile, M., and Rashid, M., 2020, Trend Analysis and Forecasting of Water Level in Mtera Dam Using Exponential Smoothing. International Journal of Mathematical Sciences and Computing (IJMSC). 6(4):26-34, DOI: 10.5815/ijMSC.2020.04.03
Goodwin, P., 2010, The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong. Foresight: International Journal of Applied Forecasting, International Institute of Forecasters, 19: 30-33.
Khairina, D. M., Maharani, S., Widagdo, P. P., Ramlawati, and Hatta, H. R. (2020). Forecasting Model of Amount of Water Production Using Double Moving Average Method, in the 3rd International Conference on Computer and Informatics Engineering (IC2IE), Yogyakarta, Indonesia, pp. 167-170, doi: 10.1109/IC2IE50715.2020.9274603.
Heydari, M., Ghadim, H. B., Rashidi, M., and Noori, M., 2020, Application of Holt-Winters Time Series Models for Predicting Climatic Parameters (Case Study: Robat Garah-Bil Station, Iran). Pol. J. Environ. Stud. 29(1): 617-627.
The Tourism Authority of Thailand (TAT), Available Source:https://thai.tourismthailand. org/Attraction/ March 22, 2022. (in Thai)