Artificial Intelligence Techniques as Detection Tests for the Identification of Shifts in Hydrometric Data

Abstract

This paper presents the development of tests based on one artificial intelligence technique, the Kohonen neural network, for the detection of shifts in hydrometric data. Two new Kohonen-based detection tests are developed, the classification and mapping tests, and their performance is compared with that of well-known conventional detection tests. The efficacy of the tests is demonstrated with synthetic data, for which all the statistical properties and induced shifts are known. These synthetic data are designed to replicate hydrometric data such as annual mean and maximum streamflow. The results show that all tests, conventional and Kohonen based, may be considered equally reliable. However, no one test should be used alone because all generate false diagnostics under different circumstances. Within a decision support environment, a pool of tests may be used to confirm or complement one another depending on their known strengths and weaknesses. The Kohonen-based detection tests also perform well when applied to multivariate cases (i.e., testing more than one data sequence at a time), and their performance for multivariate cases is better than that for the univariate cases.