Optimization of a hydrometric network extension using specific flow, kriging and simulated annealing

Abstract

In hydrometric stations, water levels are continuously observed and discharge rating curves are constantly updated to achieve accurate river levels and discharge observations. An adequate spatial distribution of hydrological gauging stations presents a lot of interest in linkage with the river regime characterization, water infrastructures design, water resources management and ecological survey. Due to the increase of riverside population and the associated flood risk, hydrological networks constantly need to be developed. This paper suggests taking advantage of kriging approaches to improve the design of a hydrometric network. The context deals with the application of an optimization approach using ordinary kriging and simulated annealing (SA) in order to identify the best locations to install new hydrometric gauges. The task at hand is to extend an existing hydrometric network in order to estimate, at ungauged sites, the average specific annual discharge which is a key basin descriptor. This methodology is developed for the hydrometric network of the transboundary Medjerda River in the North of Tunisia. A Geographic Information System (GIS) is adopted to delineate basin limits and centroids. The latter are adopted to assign the location of basins in kriging development. Scenarios where the size of an existing 12 stations network is alternatively increased by 1, 2, 3, 4 and 5 new station(s) are investigated using geo-regression and minimization of the variance of kriging errors. The analysis of the optimized locations

Performance evaluation of satellite-based rainfall products on hydrological modeling for a transboundary catchment in northwest Africa

“…In addition, most of the stream gauges are badly distributed over the Medjerda basin. For better spatial coverage of streamflow measurements, a recent study was conducted to identify the best locations to extend the hydrometric network in the north of Tunisia (Chebbi et al 2017).…”

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

“…Rainfall is a hydrological variable with significant spatial and temporal characteristics; therefore, the lack of existing networks and spatiotemporal characteristics of the site should be considered in the selection of potential sites. In practical applications, the kriging interpolation accuracy of the existing ground precipitation measurement stations can indicate whether the existing network needs to be supplemented [22,32,74]. In addition, remote sensing products provide more accurate relative spatial characteristics and temporal continuity, and thus can objectively represent local spatiotemporal characteristics [42].…”

Object-centred automated compliance checking: a novel, bottom-up approach

“…On the other hand, AI domain proposes a variety of methods in order to improve the search process such as metaheuristic optimization methods that have been developed based on some principles inspired by theory of evolution (Katoch et al, 2021), animal's behaviour (Eberhart & Kennedy, 1995), medicine and cell biology (Tschida & Silverberg, 2013), and natural phenomena ( (Saka & Dogan, 2012), (Kaveh, 2017)). Simulated Annealing (Kirkpatrick et al, 1983) is one of the first metaheuristic methods that has been successfully applied in many case studies and domains ( (Eglese, 1990), (Kalivas, 1992), (Román-Román & Torres-Ruiz, 2015), (Chebbi et al, 2017), (Bandyopadhyay et al, 2021)), and still utilised and developed up until now ( (Ficarella et al, 2021), (Duan et al, 2021)). This method is inspired by a process of natural optimization phenomenon which is the physical annealing of materials.…”