A feature prediction model in synthetic hydrology based on concepts of pattern recognition

Abstract

It is reasonable to consider that sequences of hydrologic data corresponding to daily, weekly, or monthly measurements occur in well‐defined groups. These groups possess collective properties of the data forming them. Such a collection of properties can be called a hydrologic pattern. A pattern is a description of an object, and the objects of concern in this paper are groups of data on hydrologic phenomena observed at regular time intervals. Hydrologic patterns describing each of these groups are expressed by n appropriate properties. Further, the dimensionality, n in number, can be reduced by considering only those characteristic properties, m in number (m≤n), that are common in all hydrologic patterns of the same category. These m characteristic properties are called features. A procedure is presented to extract information present within patterns and among patterns of the pertinent hydrologic data. In addition, on the basis of the above information, a zero‐order Markov feature prediction model is postulated. The basic assumptions of the model and their implications are presented. The model is applied to South Saskatchewan River flow data in an effort to demonstrate its usefulness in real situations.

Group-based estimation of missing hydrological data: I. Approach and general methodology

“…These features and the collection of such features can be considered equivalent to letters and words, respectively, in a written language. This interesting analogy with written language is made by Panu et al (1978) In this research the above argument is accepted and also extended as follows: If (WIN) is available, then the process of guessing whether it is complete or not is not an easy task. If it is not complete, then one letter could be missing.…”

“…Where in-filling or estimating missing data is considered, minimizing the squared error (difference between estimated and true values) becomes the overriding objective. The proposition of Panu et al (1978), which is briefly described and clarified here, can be modified for in-filling of missing data.…”

“…A clear diversion from the traditional single-valued approach in the stochastic hydrology literature is a work by Panu et al (1978) based on concepts of pattern recognition. Their proposition was made for the sake of generating synthetic hydrological sequences.…”

“…and the third is How does one test or verify the performance of the segmenting technique? Through the hydrological literature, the first two questions are addressed through the use of periodogram as a tool for determining the number of seasons or harmonics in a year (Panu et al, 1978;Khalil et al, 1998). The segmenting of hourly water demand data is done by observation only in Shvarster et al (1993).…”

Χωροχρονική Ανάλυση Προσομοίωση Και Πρόγνωση Ξηρασίας Στην Υδρολογική Λεκάνη Πηνειού Ποταμού Θεσσαλίας

“…Η εφαρμογή των μοντέλων έγινε για παροχές ποταμών λεκανών απορροής [Panu et al, 1978;Salas and Obeysekera 1982;Crespo and Mora, 1993;Shin and Salas, 2000;Hanson et al 2004;Yurekli et al 2005;Modarres, 2007;Abebe and Foerch, 2008;Fernandez et al, 2009;Durdu, 2010] και για μετεωρολογικούς δείκτες ξηρασίας [Kim and Valdes 2003;Mishra and Desai, 2005;Morid et al, 2007;Vasiliades and Loukas, 2007;Bacanli et al, 2009]. Οι Chung and Salas [2000] υπολόγισαν τις πιθανότητες των συμβάντων ξηρασίας με τη χρήση διακριτών ARMA (Discrete ARMA, DARMA) μοντέλων χαμηλής τάξης.…”