BibTex RIS Kaynak Göster

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Yıl 2015, Cilt: 8 Sayı: 3, 69 - 81, 29.07.2015

Recep Akdağ

Öz

Water demand can be defined as, the amount of water, needed by domestic, commercial, official institutions and industrial consumers. There are various factors such as population, employment situation, economic cycles, technology, weather conditions, water price and water saving programs which have important effects on the water demand. Local population growth, global warming change in the urban green spaces, industrial growth and improving living standards are increasingly becoming important in the growth of these effects. Besides, water usage behaviours of consumers have a great importance on the water demand

Anahtar Kelimeler

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Kaynakça

  • Adamowski, Jan Franklin (2008). Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks. Journal of Water Resources Planning and Management, 134, 119-128.
  • Ajbar, Abdel Hamid ve Ali, Emad (2012). Water Demand Prediction for Touristic Mecca City in Saudi Arabia Using Neural Networks. International Journal of Engineering and Applied Sciences, 6, 342-346.
  • Altunkaynak, Abdüsselam, Özger, Mehmet ve Çakmakçı, Mehmet (2005). Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach, Water Resources Management, 19, 641-654.
  • Alvisi, Stefano, Franchini, Marco, Marinelli, Alberto (2007). A Short-Term, Pattern-Based Model for Water-Demand Forecasting. Journal of Hydroinformatics, 9, 39-50.
  • Athanasiadis, Ioannis N., Mentes Alexandros K., Mitkas, Pericles A., Mylopoulos, Yiannis A. (2005). A Hybrid Agent-Based Model for Estimating Residential Water Demand. Simulation, 81, 175-187.
  • Babel, M. S., Gupta, A. D., Pradhan, P. (2007). A Multivariate Econometric Approach for Domestic Water Demand Modeling: An Application to Kathmandu, Nepal, Water Resources Management, 21, 573-589.
  • Billings, R. Bruce ve Agthe, Donald E. (1998). State-Space Versus Multiple Regression for Forecasting Urban Water Demand, Journal of Water Resources Planning and Management, March-April, 113–117.
  • Billings, R. Bruce, Jones, Clive V. (2008). Forecasting Urban Water Demand, Denver: American Water Works Association.
  • Bougadis, John, Adamowski, Kaz ve Diduch, Roman (2005). Short-Term Municipal Water Demand Forecasting, Hydrological Processes, 19, 137–148.
  • Brooks, David B. (1997). Management of Water Demand in Africa and the Middle East: Current Pratices and Future Needs, Brooks, David B., Rached, Eglal ve Saade, Maurice (Ed.). Introduction: Water Demand and Water Markets (1-10). Ottowa: International Development Research Centre.
  • Caiado, Jorge (2007). Forecasting Water Consumption in Spain Using Univariate Time Series Models, Proceedings of IEEE Spanish Computational Intelligence Society, 415-423.
  • Campisi-Pinto, Salvatore, Adamowski, Jan, Oron, Gideon (2012). Forecasting UrbanWater Demand ViaWavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy, Water Resource Management, 26, 3539–3558.
  • Cassuto, A. E., Ryan, S. (1979). Effect of Price on The Residential Demand for Water within an Agency, Journal of the American Water Resources Association, 15, 345-353.
  • Davis, William Y. (2003). Water Demand Forecast Methodology for California Water Planning Areas-Work Plan and Model Review, California Bay-Delta Authority, California.
  • Fırat, Mahmut, Yurdusev, M. Ali, Mermer, Mutlu (2008). Uyarlamalı Sinirsel Bulanık Mantık Yaklaşımı ile Aylık Su Tüketiminin Tahmini, Gazi Üniv. Müh. Mim. Fak. Dergisi, 23, 449- 457.
  • Froukh, M. Luay (2001). Decision-Support System for Domestic Water Demand Forecasting and Management, Water Resources Management, 15, 363–382.
  • Ghiassi, M., Zimbra, David K., Saidane, H. (2008). Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model, Journal of Water Resources Planning and Management, 134, 138-146.
  • Hansen, R. D. ve Narayanan, R. (1981). Monthly Time Series Model of Municipal Water Demand. Water Resources Bulletin, 17, 578-585.
  • Herrera Manuel, Torgo Luis, Izquierdo Joaquin, Perez-Garcia Rafael (2010). Predictive Models for Forecasting Hourly Urban Water Demand, Journal of Hidrology, 141-150.
  • Howe, C. W. ve Linaweaver, F. P. (1967). The Impact of Price on Residential Water Demand and Its Relation to Systems Design, Water Resources Research, 3, 13-22.
  • Jain, Ashu ve Ormsbee, Lindell E. (2002). Short-Term Water Demand Forecast Modeling Techniques-Conventional Methods Versus AI, American Water Works Association Journal, 94, 64-72.
  • Jain, Ashu, Varshney, Ashish Kumar, Joshi, Umesh Chandra (2001). Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks, Water Resources Management, 15, 299-321.
  • Jowitt, Paul W. ve Xu, Chengchao (1992). Demand Forecasting for Water Distribution Systems, Civil Engineering System, 9, 105-121.
  • Kobu, Bülent (1998). Üretim Yönetimi (10. Baskı). İstanbul: Avcıol Basın Yayın.
  • Liu, Junguo, Savenije, Hubert H.G. ve Xu, Jianxin (2003). Forecast of Water Demand in Weinan City in China Using WDF-ANN Model, Physics and Chemistry of the Earth, 28, 219-224.
  • Maidment, David R., Miaou, Shaw-Pin, Crawford, Melba M. (1985). Transfer Function Models of Daily Urban Water Use, Water Resources Research, 21, 425–432.
  • Molino, B., Rasulo, G. ve Taglialatela, L. (1996). Forecast Model of Water Consumption for Naples, Water Resources Management, 10, 321-332.
  • Msiza, Ishmael S., Nelwamondo, Fulufhelo V. ve Marwala, Tshilidzi (2007). Water Demand Forecasting Using Multi-layer Perceptron and Radial Basis Functions, Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA. August, 12-17.
  • Nasseri, Mohsen, Moeini, Ali, Tabesh, Massoud (2011). Forecasting Monthly Urban Water Demand Using Extended Kalman Filter and Genetic Programming, Expert Systems with Applications, 38, 7387–7395.
  • Qi, Cheng ve Chang, Ni-Bin (2011). System Dynamics Modeling for Municipal Water Demand Estimation in an Urban Region Under Uncertain Economic Impacts, Journal of Environmental Management, 92, 1628-1641.
  • White, Stuart, Robinson, Jim, Cordell, Dana, Jho, Meenakshi, Milne, Geoff (2003). Urban Water Demand Forecasting and Demand Management: Research Needs Review and Recommendaitons, Astralia: Water Services Association of Australia.
  • Wu, Li ve Zhou, Huicheng (2010). Urban Water Demand Forecasting Based on HP Filter and Fuzzy Neural Network, Journal of Hydroinformatics, 12, 172-184.
  • Yurdusev, Mehmet Ali, Firat, Mahmut, Mermer, Mutlu, Turan, Mustafa Erkan (2009). Water Use Prediction by Radial and Feed-forward Neural Mets, Water Management, 162, 179-188.
  • Zhou, S. L., McMahon, T. A., Walton, A., Lewis, J. (2002). Forecasting Operational Demand for an Urban Water Supply Zone, Journal of Hydrology, 259, 189-202.

KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ

Yıl 2015, Cilt: 8 Sayı: 3, 69 - 81, 29.07.2015

Recep Akdağ

Öz

Su talebi, evsel, ticari, resmi kurum ve endüstriyel tüketim gruplarının ihtiyaç duyduğu su miktarı olarak tanımlanabilir. Su talebi üzerinde; nüfus, istihdam, ekonomik döngüler, teknoloji, hava koşulları, fiyat ve koruma programları gibi çeşitli faktörler önemli etkilere sahiptirler. Bu etkilerin artmasında yerel nüfus artışı, küresel ısınma, kentsel yeşil alan miktarındaki değişim, endüstriyel büyüme ve yaşam standartlarındaki ilerleme gibi çeşitli faktörler giderek önem kazanmaktadır. Bununla birlikte, su talebi üzerinde tüketicilerin su kullanım davranışları oldukça büyük öneme sahiptir.

Günümüzde birçok ülke için su azlığı (kıtlığı), temel bir problem haline gelmiştir. Bu nedenle, su yönetiminde verimlilik sağlamak için su politikaları ve alışkanlıkların gözden geçirilmesi gerekmektedir. Bu durum ayrıca, su sistemlerinin daha iyi planlanmasını ve tasarımını, daha etkin işletimini ve yönetimini gündeme getirmiştir. Bunun içinde doğru su talep tahmini anahtar konumdadır. Su talep tahmini genellikle kısa, orta ve uzun dönem şeklinde planlanır. Tahmin dönemleri kullanım amaçlarına, tahmin modeli tiplerine ve farklı güvenilirlik seviyelerine göre değişiklik göstermektedir.

Anahtar Kelimeler

Su Su Talebi Su Talep Tahmini

Kaynakça

  • Adamowski, Jan Franklin (2008). Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks. Journal of Water Resources Planning and Management, 134, 119-128.
  • Ajbar, Abdel Hamid ve Ali, Emad (2012). Water Demand Prediction for Touristic Mecca City in Saudi Arabia Using Neural Networks. International Journal of Engineering and Applied Sciences, 6, 342-346.
  • Altunkaynak, Abdüsselam, Özger, Mehmet ve Çakmakçı, Mehmet (2005). Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach, Water Resources Management, 19, 641-654.
  • Alvisi, Stefano, Franchini, Marco, Marinelli, Alberto (2007). A Short-Term, Pattern-Based Model for Water-Demand Forecasting. Journal of Hydroinformatics, 9, 39-50.
  • Athanasiadis, Ioannis N., Mentes Alexandros K., Mitkas, Pericles A., Mylopoulos, Yiannis A. (2005). A Hybrid Agent-Based Model for Estimating Residential Water Demand. Simulation, 81, 175-187.
  • Babel, M. S., Gupta, A. D., Pradhan, P. (2007). A Multivariate Econometric Approach for Domestic Water Demand Modeling: An Application to Kathmandu, Nepal, Water Resources Management, 21, 573-589.
  • Billings, R. Bruce ve Agthe, Donald E. (1998). State-Space Versus Multiple Regression for Forecasting Urban Water Demand, Journal of Water Resources Planning and Management, March-April, 113–117.
  • Billings, R. Bruce, Jones, Clive V. (2008). Forecasting Urban Water Demand, Denver: American Water Works Association.
  • Bougadis, John, Adamowski, Kaz ve Diduch, Roman (2005). Short-Term Municipal Water Demand Forecasting, Hydrological Processes, 19, 137–148.
  • Brooks, David B. (1997). Management of Water Demand in Africa and the Middle East: Current Pratices and Future Needs, Brooks, David B., Rached, Eglal ve Saade, Maurice (Ed.). Introduction: Water Demand and Water Markets (1-10). Ottowa: International Development Research Centre.
  • Caiado, Jorge (2007). Forecasting Water Consumption in Spain Using Univariate Time Series Models, Proceedings of IEEE Spanish Computational Intelligence Society, 415-423.
  • Campisi-Pinto, Salvatore, Adamowski, Jan, Oron, Gideon (2012). Forecasting UrbanWater Demand ViaWavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy, Water Resource Management, 26, 3539–3558.
  • Cassuto, A. E., Ryan, S. (1979). Effect of Price on The Residential Demand for Water within an Agency, Journal of the American Water Resources Association, 15, 345-353.
  • Davis, William Y. (2003). Water Demand Forecast Methodology for California Water Planning Areas-Work Plan and Model Review, California Bay-Delta Authority, California.
  • Fırat, Mahmut, Yurdusev, M. Ali, Mermer, Mutlu (2008). Uyarlamalı Sinirsel Bulanık Mantık Yaklaşımı ile Aylık Su Tüketiminin Tahmini, Gazi Üniv. Müh. Mim. Fak. Dergisi, 23, 449- 457.
  • Froukh, M. Luay (2001). Decision-Support System for Domestic Water Demand Forecasting and Management, Water Resources Management, 15, 363–382.
  • Ghiassi, M., Zimbra, David K., Saidane, H. (2008). Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model, Journal of Water Resources Planning and Management, 134, 138-146.
  • Hansen, R. D. ve Narayanan, R. (1981). Monthly Time Series Model of Municipal Water Demand. Water Resources Bulletin, 17, 578-585.
  • Herrera Manuel, Torgo Luis, Izquierdo Joaquin, Perez-Garcia Rafael (2010). Predictive Models for Forecasting Hourly Urban Water Demand, Journal of Hidrology, 141-150.
  • Howe, C. W. ve Linaweaver, F. P. (1967). The Impact of Price on Residential Water Demand and Its Relation to Systems Design, Water Resources Research, 3, 13-22.
  • Jain, Ashu ve Ormsbee, Lindell E. (2002). Short-Term Water Demand Forecast Modeling Techniques-Conventional Methods Versus AI, American Water Works Association Journal, 94, 64-72.
  • Jain, Ashu, Varshney, Ashish Kumar, Joshi, Umesh Chandra (2001). Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks, Water Resources Management, 15, 299-321.
  • Jowitt, Paul W. ve Xu, Chengchao (1992). Demand Forecasting for Water Distribution Systems, Civil Engineering System, 9, 105-121.
  • Kobu, Bülent (1998). Üretim Yönetimi (10. Baskı). İstanbul: Avcıol Basın Yayın.
  • Liu, Junguo, Savenije, Hubert H.G. ve Xu, Jianxin (2003). Forecast of Water Demand in Weinan City in China Using WDF-ANN Model, Physics and Chemistry of the Earth, 28, 219-224.
  • Maidment, David R., Miaou, Shaw-Pin, Crawford, Melba M. (1985). Transfer Function Models of Daily Urban Water Use, Water Resources Research, 21, 425–432.
  • Molino, B., Rasulo, G. ve Taglialatela, L. (1996). Forecast Model of Water Consumption for Naples, Water Resources Management, 10, 321-332.
  • Msiza, Ishmael S., Nelwamondo, Fulufhelo V. ve Marwala, Tshilidzi (2007). Water Demand Forecasting Using Multi-layer Perceptron and Radial Basis Functions, Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA. August, 12-17.
  • Nasseri, Mohsen, Moeini, Ali, Tabesh, Massoud (2011). Forecasting Monthly Urban Water Demand Using Extended Kalman Filter and Genetic Programming, Expert Systems with Applications, 38, 7387–7395.
  • Qi, Cheng ve Chang, Ni-Bin (2011). System Dynamics Modeling for Municipal Water Demand Estimation in an Urban Region Under Uncertain Economic Impacts, Journal of Environmental Management, 92, 1628-1641.
  • White, Stuart, Robinson, Jim, Cordell, Dana, Jho, Meenakshi, Milne, Geoff (2003). Urban Water Demand Forecasting and Demand Management: Research Needs Review and Recommendaitons, Astralia: Water Services Association of Australia.
  • Wu, Li ve Zhou, Huicheng (2010). Urban Water Demand Forecasting Based on HP Filter and Fuzzy Neural Network, Journal of Hydroinformatics, 12, 172-184.
  • Yurdusev, Mehmet Ali, Firat, Mahmut, Mermer, Mutlu, Turan, Mustafa Erkan (2009). Water Use Prediction by Radial and Feed-forward Neural Mets, Water Management, 162, 179-188.
  • Zhou, S. L., McMahon, T. A., Walton, A., Lewis, J. (2002). Forecasting Operational Demand for an Urban Water Supply Zone, Journal of Hydrology, 259, 189-202.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Recep Akdağ

Yayımlanma Tarihi 29 Temmuz 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 8 Sayı: 3

Kaynak Göster

APA Akdağ, R. (2015). KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ. Niğde Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 8(3), 69-81.
AMA Akdağ R. KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. Temmuz 2015;8(3):69-81.
Chicago Akdağ, Recep. “KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ”. Niğde Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi 8, sy. 3 (Temmuz 2015): 69-81.
EndNote Akdağ R (01 Temmuz 2015) KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 8 3 69–81.
IEEE R. Akdağ, “KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ”, Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, c. 8, sy. 3, ss. 69–81, 2015.
ISNAD Akdağ, Recep. “KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ”. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 8/3 (Temmuz 2015), 69-81.
JAMA Akdağ R. KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2015;8:69–81.
MLA Akdağ, Recep. “KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ”. Niğde Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, c. 8, sy. 3, 2015, ss. 69-81.
Vancouver Akdağ R. KENTSEL SU SUNUMUNDA BİR YÖNETİM ARACI OLARAK SU TALEP TAHMİNİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2015;8(3):69-81.
 Kapak Resmi İndir