scholarly journals A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data

2001 ◽  
Vol 3 (2) ◽  
pp. 71-89 ◽  
Author(s):  
Patrick Reed ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Monitoring ◽  
Historical Data ◽  
Pareto Frontier ◽  
Monitoring Network ◽  
Cost Effective ◽  
Local Concentration ◽  
Decision Space ◽  
Estimation Errors ◽  
Concentration Estimation

This study presents a methodology for quantifying the tradeoffs between sampling costs and local concentration estimation errors in an existing groundwater monitoring network. The method utilizes historical data at a single snapshot in time to identify potential spatial redundancies within a monitoring network. Spatially redundant points are defined to be monitoring locations that do not appreciably increase local estimation errors if they are not sampled. The study combines nonlinear spatial interpolation with the nondominated sorted genetic algorithm (NSGA) to identify the tradeoff curve (or Pareto frontier) between sampling costs and local concentration estimation errors. Guidelines are given for using theoretical relationships from the field of genetic and evolutionary computation for population sizing and niching to ensure that the NSGA is competently designed to navigate the problem's decision space. Additionally, both a selection pressure analysis and a niching-based elitist enhancement of the NSGA are presented, which were integral to the algorithm's efficiency in quantifying the Pareto frontier for costs and estimation errors. The elitist NSGA identified 34 of 36 members of the Pareto optimal set attained from enumerating the monitoring application's decision space; this represents a substantial improvement over the standard NSGA, which found at most 21 of 36 members.

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.

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 Designing optimal greenhouse gas observing networks that consider performance and cost

2014 ◽  
Vol 4 (2) ◽  
pp. 705-749
Author(s):  
D. D. Lucas ◽  
C. Yver Kwok ◽  
P. Cameron-Smith ◽  
H. Graven ◽  
D. Bergmann ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Atmospheric Transport ◽  
Pareto Frontier ◽  
Ghg Emissions ◽  
Emission Rates ◽  
Monitoring Networks ◽  
Transport Models ◽  
Hfc 134A ◽  
Scientific Accuracy ◽  
Better Than

Abstract. Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

scholarly journals Presence of pharmaceutical compounds in groundwater with respect to land use in the vicinity of sampling sites

Geologos ◽  
2019 ◽  
Vol 25 (3) ◽  
pp. 231-240 ◽  
Author(s):  
Anna Kuczyńska
Keyword(s):  
Land Use ◽  
Ground Water ◽  
Groundwater Monitoring ◽  
Monitoring Network ◽  
Antimicrobial Drugs ◽  
Sources Of Pollution ◽  
Groundwater Samples ◽  
Land Use Types ◽  
The Impact ◽  
Active Substances

Abstract The present paper discusses the results of an analysis of the impact of land use on the distribution of pharmaceuticals in groundwater samples collected during a pilot study of the contents of pharmaceuticals and hormones in ground-water taken from the national groundwater monitoring network of the Polish Geological Institute - National Research Institute. Samples were collected during monitoring campaigns from 160 groundwater monitoring sites in various land use types in 2016 and 2017. Samples were analysed for a total of 34 active substances, including natural and synthetic oestrogen hormones, cardiovascular and respiratory medications, analgesics and anti-inflammatories, antidepressants, antimicrobial drugs and anti-epileptics. Our study confirmed the presence of pharmaceuticals in 53 per cent of ground-water samples taken. Data show variations in the distribution of pharmaceuticals depending on land use type, which can thus be employed in pressure analysis and identification of sources of pollution.

A computer simulation model of forest fire suppression with air tankers

1979 ◽  
Vol 9 (3) ◽  
pp. 390-398 ◽  
Author(s):  
A. J. Simard
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Historical Data ◽  
Fire Suppression ◽  
Cost Effective ◽  
Computer Simulation Model ◽  
Fire Dynamics ◽  
Data Output ◽  
The One ◽  
Fire Ground

A computer simulation model which evaluates air tanker productivity and effectiveness is described. Three hundred equations are required to define the model, which consists of five components: administration, the environment, the fire, ground suppression, and air tanker utilization. AIRPRO, a computer program based on the model, tests various combinations of air tanker resources and tactics and selects the one which minimizes suppression cost plus damage caused by fire. The program contains four loops: the fire, the tactic, the event, and fire dynamics (growth and suppression). The environmental, fire, and ground suppression components were validated by comparing model output with historical data. Output of the air tanker component was examined for reasonableness, compared with previous research, and a sensitivity analysis was performed. It is concluded that an assumption of model validity is reasonable. In applying the model in New Brunswick, it was found that medium sized land-based air tankers were the most cost effective, followed by small land-based air tankers. It was also found that a fleet of three or four aircraft would be optimum.

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