scholarly journals Multi-objective optimization of groundwater monitoring network using a probability Pareto genetic algorithm and entropy method (case study: Silakhor plain)

2020 ◽  
Author(s):  
Mehdi Komasi ◽  
Hesam Goudarzi
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Monitoring ◽  
Monitoring Network ◽  
Entropy Theory ◽  
Monitoring Networks ◽  
Nsga Ii ◽  
Potential Wells ◽  
Optimal Monitoring ◽  
Observation Wells ◽  
Optimal Monitoring Network

Abstract Optimal groundwater monitoring networks have an important role in water resources management. For this purpose, two scenarios were presented. The first scenario designs a monitoring network and the second scenario chooses optimal wells from the existing ones in the study area of the monitoring network. At the first step, a database including groundwater elevation in potential wells was produced using the Kriging method. The optimal monitoring network in the first scenario was determined by preset conventions and found by the non-dominated sorting genetic algorithm (NSGA-II). In the second scenario, the optimal monitoring network was determined by entropy theory through calculating entropy for each of the 29 observation wells. Finally, the first scenario obtained a network with 12 observation stations showing root mean square error (RMSE) value given as 0.61 m. Comparison between entropy of rainfall and groundwater level time series in the first scenario had the same variation. The optimal monitoring network in the first scenario has been able to reduce the number of monitoring stations by 60% in comparison with the existing observation network. The second scenario used entropy theory and calculated the energy of each of the 29 observation wells which obtained a monitoring network with 11 stations.

scholarly journals Optimal design of groundwater-level monitoring networks

2017 ◽  
Vol 19 (6) ◽  
pp. 920-929 ◽  
Author(s):  
Fahimeh Mirzaie-Nodoushan ◽  
Omid Bozorg-Haddad ◽  
Hugo A. Loáiciga
Keyword(s):  
Groundwater Level ◽  
Groundwater Monitoring ◽  
Design Method ◽  
Monitoring Network ◽  
Optimization Method ◽  
Monitoring Networks ◽  
Nsga Ii ◽  
Design Data ◽  
Groundwater Levels ◽  
Level Monitoring

Abstract Groundwater monitoring plays a significant role in groundwater management. This study presents an optimization method for designing groundwater-level monitoring networks. The proposed design method was used in the Eshtehard aquifer, in central Iran. Three scenarios were considered to optimize the locations of the observation wells: (1) designing new monitoring networks, (2) redesigning existing monitoring networks, and (3) expanding existing monitoring networks. The kriging method was utilized to determine groundwater levels at non-monitoring locations for preparing the design data base. The optimization of the groundwater monitoring network had the objectives of (1) minimizing the root mean square error and (2) minimizing the number of wells. The non-dominated sorting genetic algorithm (NSGA-II) was applied to optimize the network. Inverse distance weighting interpolation was used in NSGA-II to estimate the groundwater levels while optimizing network design. Results of the study indicate that the proposed method successfully optimizes the design of groundwater monitoring networks that achieve accuracy and cost-effectiveness.

Geostatistical screening of flood events in the groundwater levels of the diverted inner delta of the Danube River: implications for river bed clogging

2018 ◽  
Vol 10 (1) ◽  
pp. 64-78 ◽  
Author(s):  
Balázs Trásy ◽  
Tamás Garamhegyi ◽  
Péter Laczkó-Dobos ◽  
József Kovács ◽  
István Gábor Hatvani
Keyword(s):  
Spatial Autocorrelation ◽  
Groundwater Monitoring ◽  
Shallow Groundwater ◽  
Monitoring Network ◽  
Danube River ◽  
Monitoring Networks ◽  
Flood Events ◽  
Raw Data ◽  
Monitoring Wells ◽  
Autocorrelation Structure

Abstract The efficient operation of shallow groundwater (SGW) monitoring networks is crucial to water supply, in-land water protection, agriculture and nature conservation. In the present study, the spatial representativity of such a monitoring network in an area that has been thoroughly impacted by anthropogenic activity (river diversion/damming) is assessed, namely the Szigetköz adjacent to the River Danube. The main aims were to assess the spatial representativity of the SGW monitoring network in different discharge scenarios, and investigate the directional characteristics of this representativity, i.e. establish whether geostatistical anisotropy is present, and investigate how this changes with flooding. After the subtraction of a spatial trend from the time series of 85 shallow groundwater monitoring wells tracking flood events from 2006, 2009 and 2013, variography was conducted on the residuals, and the degree of anisotropy was assessed to explore the spatial autocorrelation structure of the network. Since the raw data proved to be insufficient, an interpolated grid was derived, and the final results were scaled to be representative of the original raw data. It was found that during floods the main direction of the spatial variance of the shallow groundwater monitoring wells alters, from perpendicular to the river to parallel with it for over a period of about two week. However, witht the passing of the flood, this returns to its original orientation in ~2 months. It is likely that this process is related first to the fast removal of clogged riverbed strata by the flood, then to their slower replacement. In addition, the study highlights the importance of assessing the direction of the spatial autocorrelation structure of shallow groundwater monitoring networks, especially if the aim is to derive interpolated maps for the further investigation or modeling of flow.

scholarly journals A decision support tool for optimising groundwater-level monitoring networks using an adaptive genetic algorithm

2021 ◽  
Author(s):  
Antonios Parasyris ◽  
Katerina Spanoudaki ◽  
Emmanouil A. Varouchakis ◽  
Nikolaos A. Kampanis
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Level ◽  
Monitoring Network ◽  
Decision Support Tool ◽  
Adaptive Method ◽  
Adaptive Genetic Algorithm ◽  
Monitoring Networks ◽  
Support Tool ◽  
Groundwater Levels ◽  
Geostatistical Methods

Abstract Mapping of the spatial variability of sparse groundwater-level measurements is usually achieved by means of geostatistical methods. This work tackles the problem of deficient sampling of an aquifer, by employing an innovative integer adaptive Genetic Algorithm (iaGA) coupled with geostatistical modelling by means of ordinary kriging, to optimise the monitoring network. Fitness functions based on three different errors are used for removing a constant number of boreholes from the monitoring network. The developed methodology has been applied to the Mires basin in Crete, Greece. The methodological improvement proposed concerns the adaptive method for the GA, which affects the crossover–mutation fractions depending on the stall parameter, aiming at higher accuracy and faster convergence of the GA. The initial dataset consists of 70 monitoring boreholes and the applied methodology shows that as many as 40 boreholes can be removed, while still retaining an accurate mapping of groundwater levels. The proposed scenario for optimising the monitoring network consists of removing 30 boreholes, in which case the estimated uncertainty is considerably smaller. A sensitivity analysis is conducted to compare the performance of the standard GA with the proposed iaGA. The integrated methodology presented is easily replicable for other areas for efficient monitoring networks design.

Optimal monitoring network design for a land use regression study in Lanzhou, China

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Lan Jin* ◽  
Jesse Berman ◽  
Kangning Huang ◽  
Yawei Zhang ◽  
Xibao Xu ◽  
...  
Keyword(s):  
Land Use ◽  
Network Design ◽  
Monitoring Network ◽  
Land Use Regression ◽  
Monitoring Network Design ◽  
Optimal Monitoring ◽  
Optimal Monitoring Network

Optimal monitoring network designs

1984 ◽  
Vol 2 (4) ◽  
pp. 223-227 ◽  
Author(s):  
W.F. Caselton ◽  
J.V. Zidek
Keyword(s):  
Monitoring Network ◽  
Optimal Monitoring ◽  
Optimal Monitoring Network

OPTIMAL MONITORING NETWORK DESIGN FOR GROUNDWATER QUANTITY AND QUALITY: A CASE STUDY

2017 ◽  
Author(s):  
Socrates Alonso Torres ◽  
◽  
Graciela S. Herrera ◽  
Edgar Ulisses Salazar-Cortes ◽  
Hugo Enrique Junez-Ferreira ◽  
...  
Keyword(s):  
Network Design ◽  
Monitoring Network ◽  
Monitoring Network Design ◽  
Optimal Monitoring ◽  
Groundwater Quantity ◽  
Optimal Monitoring Network
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