Application of a data mining approach to derive operating rules for the Eleviyan irrigation reservoir

Sattari M. T., APAYDIN H., Ozturk F., BAYKAL N.

LAKE AND RESERVOIR MANAGEMENT, cilt.28, sa.2, ss.142-152, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 28 Sayı: 2
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1080/07438141.2012.678927
  • Dergi Adı: LAKE AND RESERVOIR MANAGEMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.142-152
  • Anahtar Kelimeler: data mining, decision tree, Monte Carlo simulation, operating rules, operational research, optimization, water resource management
  • Ankara Üniversitesi Adresli: Evet

Özet

Optimum irrigation reservoir operation is a significant issue for water management decision makers. In this study, we used 4 different datasets of monthly amounts of water to be released from the Eleviyan irrigation reservoir in Iran as inputs in a data mining model; "if-conditional" operating rules were determined as outputs. Operating rules derived from data mining and rules obtained from an optimization model were in high concordance, especially between the 2 datasets from the preconstruction period, with differences in only 12 of 252 instances (252 months) used to compare the 2 methods. Operating rules determined for the preconstruction period using the data mining method were also consistent with optimum operating rules determined either by optimization or Monte Carlo simulation. Thus, we concluded that the decision tree subtechnique of data mining is an appropriate method for determining meaningful operating rules for the reservoir.