Entropy and generalized least square methods in assessment of the regional value of streamgages

Abstract

"Entropy and generalized least square methods in assessment of the regional value of streamgages" (2003 AbstractThe Illinois State Water Survey performed a study to assess the streamgaging network in the State of Illinois. One of the important aspects of the study was to assess the regional value of each station through an assessment of the information transfer among gaging records for low, average, and high flow conditions. This analysis was performed for the main hydrologic regions in the State, and the stations were initially evaluated using a new approach based on entropy analysis. To determine the regional value of each station within a region, several information parameters, including total net information, were defined based on entropy. Stations were ranked based on the total net information. For comparison, the regional value of the same stations was assessed using the generalized least square regression (GLS) method, developed by the US Geological Survey. Finally, a hybrid combination of GLS and entropy was created by including a function of the negative net information as a penalty function in the GLS. The weights of the combined model were determined to maximize the average correlation with the results of GLS and entropy. The entropy and GLS methods were evaluated using the high-flow data from southern Illinois stations. The combined method was compared with the entropy and GLS approaches using the high-flow data from eastern Illinois stations. q

Evaluating hydrometric networks for prediction in ungauged basins: a new methodology and its application to England and Wales

“…Black et al 1994;Markus et al 2003). A key specification of the new methodology was that it could be automated and applied rapidly to a national network of upwards of 1000 gauging stations.…”

“…Another important criterion for development was that the technique should be relatively simple and should deliver results that are easy to interpret, which favoured a scoring system based on catchment and station attributes rather than the overtly statistical approach favoured by many previous studies; as discussed in the introduction, many methods rely on concepts such as information theory (e.g. Yang and Burn, 1994;Markus et al 2003;Mishra and Coulibaly, 2010) which are less readily interpretable by non-specialists.…”

“…Moss and Tasker, 1991), or assessing the information-transfer capability of a network (e.g. Yang and Burn, 1994;Markus et al 2003;Mishra and Coulibaly, 2010). Other methods have focused on catchment characteristics; Laizé (2004), for example, developed a Representative Catchment Index for assessing the similarity of a gauged catchment to a reference area in terms of land cover and elevation characteristics.…”

A framework for accurate geospatial modeling using image ranking and machine learning

“…In order to accommodate the fact that many variables in Earth sciences are categorical, for instance, land use, land cover maps, soil maps or recharge/discharge maps, we adopted a Mutual Information measure based on Shannon's information theory (Shannon 1948 information content of a variable. The measure has been applied in hydrology in the past for studying the value of stream gauges in a network (Markus et al 2003;Sarlak & Sorman 2006) or for designing optimal networks (Singh 1997). We will describe the mathematical underpinning of the unsupervised ranking of variables using mutual information measure.…”

Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy

“…Applications to groundwater quality monitoring networks, stream gauge networks, and water distribution networks have increased in recent years. The methods used in network research related to entropy include least square methods and entropy [4], kriging [5], information entropy [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], and combined kriging and information entropy [23][24][25]. In particular, the information entropy approach has been widely adopted since the 1970s for hydrologic data collection network design and uncertainty evaluation [26][27][28][29][30][31][32][33].…”