Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting

Abstract

The non-parametric regression method of K-nearest neighbour (K-NN) has been used in a variety of eco-hydrological issues. In this study, some techniques are presented to improve the accuracy of the K-NN method in forecasting the accumulated 9-month inflow of Zayandeh-rud dam in Iran, from winter to the end of the following summer. The improving techniques considered at current study include: 1) selection of the best data pre-processing functions, 2) selecting the best number of neighbour, 3) selecting the best distance functions, 4) specifying the best weights of predictors at distance functions, and 5) Adding the ability of extrapolation to K-NN using a simple method. Final results show that the use of mentioned techniques can improve the accuracy of K-NN’s forecast meaningfully. The results of goodness-of-fit criteria for the optimized K-NN in comparison with a regular K-NN can present an increase of 31% at correlation coefficient (from 65% to 96%), a decrease from 31 to 8 at volume error, and finally a drop from 54 to 25 at root mean square error.