Öz
Water resources have played a significant role in the location of cities throughout history. However, today, global warming and the disruption of the ecological system have led to a decrease in annual precipitation. Additionally, rapid population growth and indiscriminate water consumption have made the efficient use of water resources an environmental necessity. Therefore, water consumption management is crucial for sustainability and the continuity of future generations. In this context, the analysis of factors affecting water consumption and the prediction of future demands are vital issues.
Research conducted on water consumption and forecasts in a large metropolis like Istanbul, Turkey's most populous city, serves as an important example for water supply and consumption management. In water supply and consumption management, water predictions are utilized in water distribution network studies and operational plans. Developing a water management strategy that focuses on predicting future water consumption demand provides an opportunity to optimize water retention, storage, and treatment costs.
In this study, monthly water consumption data provided by ISKI (Istanbul Water and Sewerage Administration) and annual population data obtained from TUIK (Turkish Statistical Institute) for Istanbul have been analyzed. Based on the analysis results, models were created using the Holt-Winters and Box-Jenkins methods, and annual water consumption and population estimates for Istanbul until the year 2033 were made. The performance values of the generated models were compared. ARIMA (3,1,2) was selected as the best model for population estimation, and the Additive Winters' method was chosen for water consumption estimation. According to the results obtained, the per capita water consumption in the year 2010 was 58.69 m^3/person, while it is expected to reach 75.83 m^3/person by the year 2033.
Anahtar Kelimeler
Water Consumption Estimation Population Estimate Time Series Analysis Holt-Winters’ Box-Jenkins
Kaynakça
- Akdağ, R. (2016). Yapay Sinir Ağları, Destek Vektör Makineleri ve Box-Jenkins Yöntemleriyle Kentsel İçmesuyu Talebi Tahmini ve Karşılaştırmalı Analizi, Business and Economics Research Journal,123-138.
- Almanjahie, I., Chikr-Elmezouar, Z., Ahmed, B., (2019). Modeling and forecasting the household water consumption in Saudi Arabia. Applied Ecology and Environmental Research. 17. 1299-1309.
- Aslan, B., Önen, F., & Hamidi, N. (2018). Diyarbakır Kenti içme suyu ihtiyacının genetik ifadeli programlama ile modellenmesi. DÜMF Mühendislik Dergisi, 859-870.
- Boudhaouia, A., Wira, P. "SARIMA and neural network models combination for time series forecasting: Application to daily water consumption," (2022). 2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE), Ankara, Turkey, 169-174.
- Box, G., & Jenkins, G. (1970). Time Series Analysis-Forecasting and Control. San Francisco: Holden Day, 553.
- Donkor, E., Mazzuchi, T., & Soyer, R., Roberson, A., (2014). Urban Water Demand Forecasting: Review of Methods and Models. Journal of Water Resources Planning and Management. 140. 146-159.
- Dutta, A., Chakrabarti, A.,Gautam, J., "Application of SARIMA for Prediction of Water Storage Levels for a Metropolitan Area: Chennai, a Case Study," (2020). International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Marrakech, Morocco, 2020, 1-8.
- Enbeyle, W., Hamad, A., Al-Obeidi, A., Andargie, S., Gelaw, A., Markos, A., Abate, L., Alemu, B., (2022). Trend Analysis and Prediction on Water Consumption in Southwestern Ethiopia. Journal of Nanomaterials.
- Hafid, M.S. & Al-maamary, G. H. (2011). Short Term Electrical Load Forecasting Using Holt-Winters Method, Al-Rafidain Engineering, 20 (6), 15-22.
- Holt, C.C. (1957). Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. ONR Memorandum, Vol. 52, Carnegie Institute of Technology, Pittsburgh
- Jain, A., & Ormsbee, L. E. (2002). Short-term Water Demand Forecast Modeling Techniques-Conventional Methods Versus AI, American Water Works Association, 94, 64–72.
- Jorge, C., (2007). Forecasting water consumption in Spain using univariate time series models.
- Kozłowski, E., Mazurkiewicz, D., Kowalska, B., & Kowalski, D. (2018). Application of Holt-Winters method in water consumption prediction. R. K. Ryszard içinde, Innowacje w zarządzaniu i inżynierii produkcji (s. 627-634).
- Maidment, D. R., and Parzen, E., (1984). Cascade Model of Monthly Municipal Water Use, Water Resources Research, 15-23.
- Mombeni, H. A., Rezaei, S., & Nadarajah, S. (2013). Estimation of Water Demand in Iran Based on SARIMA Models. Environmental Modeling & Assessment , 559-565.
- Mousavi-Mirkalaei, P., Banihabib, M. E. (2019). An ARIMA-NARX hybrid model for forecasting urban water consumption (case study: Tehran metropolis), Urban Water Journal, 1–12. Palma, W. (2016). Time series Analysis, Wiley, 616.
- Razali, S. N. A. M., Rusiman, M. S., Zawawi, N. I., & Arbin, N. (2018). Forecasting of Water Consumptions Expenditure Using Holt-Winter’s and ARIMA, Journal of Physics: Conference Series, 995, 012041.
- Schwarz, G. (1978).Estimating the Dimension of a Model, Annals of Statistics, 6, 461-464.
- Shmueli, G., Lichtendahl Jr, K. C. (2016). Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics),208. Wang, X., Tian, W., & Liao, Z. (2021). Statistical comparison between SARIMA and ANN’s performance for surface water quality time series prediction. Environmental Science and Pollution Research, 28(25), 33531–33544. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages, Management Science, 6(3), 324–342.
Öz
Su kaynakları, tarih boyunca şehirlerin konumlandırılmasında önemli bir rol oynamaktadır. Ancak günümüzde küresel ısınma ve ekolojik sistemin bozulması, yıllık yağışların azalmasına yol açmıştır. Ayrıca, hızlı nüfus artışı ve bilinçsiz su tüketimi de su kaynaklarının verimli kullanılmasını çevresel zorunluluk haline getirmiştir. Dolayısıyla, sürdürülebilirlik ve gelecek nesillerin devamı için su tüketimi yönetimi önem arz etmektedir. Bu bağlamda, su tüketimini etkileyen faktörlerin analizi ve gelecekteki taleplerin tahmin edilmesi hayati bir konudur.
Türkiye’nin en kalabalık şehri olan İstanbul gibi büyük bir metropolün, su tüketimi ve tahminleri üzerine yapılan araştırmalar su temini ve tüketimi yönetimine önemli bir örnektir.
Su temini ve tüketimi yönetiminde, su dağıtım şebekesi çalışmalarında ve operasyon planlarında su tahminlerinden yararlanılır. Gelecekteki su tüketimi talebini tahmin etmeye odaklanan bir su yönetiminin stratejisi hazırlamak; su tutma, depolama ve arıtma maliyetlerini optimize etme fırsatı sağlar.
Bu çalışmada İSKİ tarafından sağlanan İstanbul’un aylık su tüketim verileri ve TÜİK’ten elde edilen İstanbul’un yıllık nüfus verileri analiz edilmiştir. Elde edilen analiz sonuçlarına göre, Holt-Winters ve Box-Jenkins yöntemleri kullanılarak modeller oluşturulmuş olup, İstanbul ilindeki su tüketimine dair 2033 yılına kadar olan yıllık su tüketimi tahmini ve yıllık nüfus tahmini yapılmıştır. Oluşturulan modellerin performans değerleri karşılaştırılmıştır. En iyi tahmin modelleri olarak nüfus tahmini için ARIMA (3,1,2) ve su tüketimi tahmini için Toplamsal Winters’ yöntemi seçilmiştir. Elde edilen sonuçlara göre, 2010 yılında kişi başına düşen su tüketim miktarı 58,69 m^3/kişi iken, 2033 yılında bu miktarın 75,83 m^3/kişi olması beklenmektedir.
Anahtar Kelimeler
Su Tüketimi Tahmini Nüfus Tahmini Zaman Serileri Analizi Holt-Winters’ Box-Jenkins
Kaynakça
- Akdağ, R. (2016). Yapay Sinir Ağları, Destek Vektör Makineleri ve Box-Jenkins Yöntemleriyle Kentsel İçmesuyu Talebi Tahmini ve Karşılaştırmalı Analizi, Business and Economics Research Journal,123-138.
- Almanjahie, I., Chikr-Elmezouar, Z., Ahmed, B., (2019). Modeling and forecasting the household water consumption in Saudi Arabia. Applied Ecology and Environmental Research. 17. 1299-1309.
- Aslan, B., Önen, F., & Hamidi, N. (2018). Diyarbakır Kenti içme suyu ihtiyacının genetik ifadeli programlama ile modellenmesi. DÜMF Mühendislik Dergisi, 859-870.
- Boudhaouia, A., Wira, P. "SARIMA and neural network models combination for time series forecasting: Application to daily water consumption," (2022). 2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE), Ankara, Turkey, 169-174.
- Box, G., & Jenkins, G. (1970). Time Series Analysis-Forecasting and Control. San Francisco: Holden Day, 553.
- Donkor, E., Mazzuchi, T., & Soyer, R., Roberson, A., (2014). Urban Water Demand Forecasting: Review of Methods and Models. Journal of Water Resources Planning and Management. 140. 146-159.
- Dutta, A., Chakrabarti, A.,Gautam, J., "Application of SARIMA for Prediction of Water Storage Levels for a Metropolitan Area: Chennai, a Case Study," (2020). International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Marrakech, Morocco, 2020, 1-8.
- Enbeyle, W., Hamad, A., Al-Obeidi, A., Andargie, S., Gelaw, A., Markos, A., Abate, L., Alemu, B., (2022). Trend Analysis and Prediction on Water Consumption in Southwestern Ethiopia. Journal of Nanomaterials.
- Hafid, M.S. & Al-maamary, G. H. (2011). Short Term Electrical Load Forecasting Using Holt-Winters Method, Al-Rafidain Engineering, 20 (6), 15-22.
- Holt, C.C. (1957). Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. ONR Memorandum, Vol. 52, Carnegie Institute of Technology, Pittsburgh
- Jain, A., & Ormsbee, L. E. (2002). Short-term Water Demand Forecast Modeling Techniques-Conventional Methods Versus AI, American Water Works Association, 94, 64–72.
- Jorge, C., (2007). Forecasting water consumption in Spain using univariate time series models.
- Kozłowski, E., Mazurkiewicz, D., Kowalska, B., & Kowalski, D. (2018). Application of Holt-Winters method in water consumption prediction. R. K. Ryszard içinde, Innowacje w zarządzaniu i inżynierii produkcji (s. 627-634).
- Maidment, D. R., and Parzen, E., (1984). Cascade Model of Monthly Municipal Water Use, Water Resources Research, 15-23.
- Mombeni, H. A., Rezaei, S., & Nadarajah, S. (2013). Estimation of Water Demand in Iran Based on SARIMA Models. Environmental Modeling & Assessment , 559-565.
- Mousavi-Mirkalaei, P., Banihabib, M. E. (2019). An ARIMA-NARX hybrid model for forecasting urban water consumption (case study: Tehran metropolis), Urban Water Journal, 1–12. Palma, W. (2016). Time series Analysis, Wiley, 616.
- Razali, S. N. A. M., Rusiman, M. S., Zawawi, N. I., & Arbin, N. (2018). Forecasting of Water Consumptions Expenditure Using Holt-Winter’s and ARIMA, Journal of Physics: Conference Series, 995, 012041.
- Schwarz, G. (1978).Estimating the Dimension of a Model, Annals of Statistics, 6, 461-464.
- Shmueli, G., Lichtendahl Jr, K. C. (2016). Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics),208. Wang, X., Tian, W., & Liao, Z. (2021). Statistical comparison between SARIMA and ANN’s performance for surface water quality time series prediction. Environmental Science and Pollution Research, 28(25), 33531–33544. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages, Management Science, 6(3), 324–342.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Endüstri Mühendisliği |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 29 Şubat 2024 |
| Gönderilme Tarihi | 19 Temmuz 2023 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 6 Sayı: 2 |
