Application of ANN and ANFIS models for reconstructing missing flow data.
Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.
Dastorani
Mohammad T
MT
Faculty of Natural Resources, Yazd University, Yazd, Iran. mdastorani@yazduni.ac.ir
Moghadamnia
Alireza
A
Piri
Jamshid
J
Rico-Ramirez
Miguel
M
D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D004784:Environmental Monitoring / Q000379:methods*; D005240:Feasibility Studies; D016571:Neural Networks, Computer; D014881:Water Supply / Q000706:statistics & numerical data