Print Email Facebook Twitter Clustered K nearest neighbor algorithm for daily inflow forecasting Title Clustered K nearest neighbor algorithm for daily inflow forecasting Author Akbari, M. Van Overloop, P.J.A.T.M. Afshar, A. Faculty Civil Engineering and Geosciences Department Watermanagement Date 2010-12-07 Abstract Instance based learning (IBL) algorithms are a common choice among data driven algorithms for inflow forecasting. They are based on the similarity principle and prediction is made by the finite number of similar neighbors. In this sense, the similarity of a query instance is estimated according to the closeness of its feature vector with those of data available in calibration data. As the selected attributes in the feature vector are determined overall on calibration data, there may be some data points whose outputs do not follow the considered attributes. In fact, output values of these inconsistent data points may be a function of some other attributes which were not considered. Therefore, for some query instances, the inconsistent points may be appeared as the neighbors while they may not really be neighbor to the query instance. They can deteriorate forecasting results especially if they are very close to the query instance with the current similarity definition. In this study a clustered K nearest neighbor (CKNN) algorithm is introduced which can capture these inconsistent data points. Similar to the inconsistent data points, CKNN can be also robust against noisy data. The proposed algorithm was shown to be effective for a synthetic linear data set corrupted by noise. In addition, the utility of the algorithm was demonstrated for daily inflow forecasting of the Karoon1 reservoir located in Iran. Subject inconsistent datainflow forecastingK nearest neighborsubtractive clusteringnoisy data To reference this document use: http://resolver.tudelft.nl/uuid:5406516b-92f0-4f27-819d-1ccd94000908 DOI https://doi.org/10.1007/s11269-010-9748-z Publisher Springer ISSN 0920-4741 Source http://www.springerlink.com/content/f278883180480831/ Source Water Resources Management, 25 (5), 2011 Part of collection Institutional Repository Document type journal article Rights (c) 2010 Springer Science+Business Media B.V. Files PDF akbari.pdf 433.13 KB Close viewer /islandora/object/uuid:5406516b-92f0-4f27-819d-1ccd94000908/datastream/OBJ/view