Effect of SOI and spatially distributed climate data on improving the accuracy of ANN, ANFIS and K-NN streamflow forecasting models

نویسندگانSaghafian, B - Anvari, S - Morid, S
نشریهExpert Systems
ارائه به نام دانشگاهTechnical and Engineering Department, Science and Research Branch Islamic Azad University
شماره صفحات367-380
شماره مجلد30
ضریب تاثیر (IF)1.43
نوع مقالهFull Paper
تاریخ انتشار2013
رتبه نشریهISI
نوع نشریهچاپی
کشور محل چاپایالات متحدهٔ امریکا

چکیده مقاله

Streamflow forecast models are essential ingredients for water resources management. Due to nonlinear nature of streamflow generation, Artificial Neural Networks (ANNs), Adaptive Neuro‐Fuzzy Inference System (ANFIS) and K‐Nearest Neighbour (K‐NN) have turned into popular forecast models. The main objective of this paper was to study the effect of various non‐commonly used data, including spatially distributed climatic data, as well as Southern Oscillation Index (SOI), on the improvement of one‐ to three‐month ahead flow forecasts in the Karun basin, Iran. Temperature maps were produced via elevation–temperature relationships, while inverse distance‐weighted interpolation was applied to generate precipitation maps. In addition, three different input architectures were constructed for ANNs and ANFIS models using cross‐correlation and principal component analysis techniques. Next, the most accurate models with input point data were identified based on the root mean squared error and mean absolute error. Finally, the effect of using spatial climatic data and SOI on the performance of the most accurate models was investigated. Results showed that distributed precipitation data improved the performance of ANN and ANFIS models, while K‐NN model accuracy was only marginally affected. Adding SOI as an additional input improved the forecast performance slightly in all lead times.

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