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Cites:

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Information Journal Paper

Title

DEVELOPMENT OF K-NEAREST NEIGHBOR REGRESSION METHOD IN FORECASTING RIVER STREAM FLOW

Pages

  107-118

Abstract

 Different statistical, non-statistical and black-box methods have been used in forecasting processes. Among statistical methods, K-nearest neighbor non-parametric regression method (K-NN) due to its natural simplicity and mathematical base is one of the recommended methods for forecasting processes. In this study, K-NN method is explained completely. Besides, development and improvement approaches such as best neighbor estimation, data transformation functions, DISTANCE FUNCTIONS and proposed EXTRAPOLATION method are described. K-NN method in company with its development approaches is used in stream flow forecasting of Zayandeh-Rud Dam upper basin. Comparing between final results of classic K-NN method and modified K-NN (number of neighbor 5, transformation function of Range Scaling, distance function of Mahanalobis and proposed EXTRAPOLATION method) shows that modified K-NN in criteria of goodness of fit, root mean square error, percentage of volume of error and correlation has had performance improvement 45%, 59% and 17% respectively. These results approve necessity of applying mentioned approaches to derive more accurate forecasts.

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  • Cite

    APA: Copy

    AZMI, MOHAMMAD, & ARAGHINEJAD, SHAHAB. (2012). DEVELOPMENT OF K-NEAREST NEIGHBOR REGRESSION METHOD IN FORECASTING RIVER STREAM FLOW. WATER AND WASTEWATER, 23(2 (82)), 107-118. SID. https://sid.ir/paper/404547/en

    Vancouver: Copy

    AZMI MOHAMMAD, ARAGHINEJAD SHAHAB. DEVELOPMENT OF K-NEAREST NEIGHBOR REGRESSION METHOD IN FORECASTING RIVER STREAM FLOW. WATER AND WASTEWATER[Internet]. 2012;23(2 (82)):107-118. Available from: https://sid.ir/paper/404547/en

    IEEE: Copy

    MOHAMMAD AZMI, and SHAHAB ARAGHINEJAD, “DEVELOPMENT OF K-NEAREST NEIGHBOR REGRESSION METHOD IN FORECASTING RIVER STREAM FLOW,” WATER AND WASTEWATER, vol. 23, no. 2 (82), pp. 107–118, 2012, [Online]. Available: https://sid.ir/paper/404547/en

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