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.

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.

A new deterministic method for groundwater mapping using a digital elevation model

2013 ◽  
Vol 13 (4) ◽  
pp. 1146-1153 ◽  
Author(s):  
Tamás Ács ◽  
Zoltán Simonffy
Keyword(s):  
Digital Elevation Model ◽  
Groundwater Level ◽  
Ground Surface ◽  
Terrestrial Ecosystems ◽  
Groundwater Depth ◽  
Deterministic Method ◽  
Groundwater Levels ◽  
Geostatistical Methods ◽  
Digital Elevation ◽  
Elevation Model

Accurate knowledge of groundwater levels and flow conditions in the vicinity of groundwater-dependent terrestrial ecosystems (GWDTE-s) is required for identifying groundwater dependency and comparing the present situation with the optimal one, as part of the status assessment of groundwaters according to the EU Water Framework Directive. Geostatistical methods (like kriging or cokriging) may result in an unrealistic groundwater level map if only a few measured data are available. In this paper a new, grid-based, deterministic method (GSGW-model) is introduced. The aim of the model is to calculate groundwater depth within the required accuracy from sparse data of monitoring wells. The basic principle of the GSGW-model is that the groundwater table is a smoothed replica of the ground surface. Hence, changes in the groundwater level between two grid points are calculated as a function of the digital elevation model (DEM) and soil properties. The GSGW-model was tested in the Nyírség region (Hungary). Results were compared with those gained by ordinary kriging and cokriging. It has been concluded that kriging overestimates the groundwater level in the low part of the test area, where wetlands are located, while the maps produced by the GSGW-model are a better fit of the real variability, providing more reliable estimates of groundwater depth in GWDTE-s as well.

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 Measuring the unseen: mobilizing citizen scientists to monitor groundwater in Nepal

2021 ◽  
Vol 193 (9) ◽  
Author(s):  
Rajaram Prajapati ◽  
Rocky Talchabhadel ◽  
Bhesh Raj Thapa ◽  
Surabhi Upadhyay ◽  
Amber Bahadur Thapa ◽  
...  
Keyword(s):  
Groundwater Level ◽  
Groundwater Monitoring ◽  
Monsoon Season ◽  
Land Use Changes ◽  
Ground Surface ◽  
Shallow Groundwater ◽  
Monitoring Network ◽  
Essential Information ◽  
Groundwater Levels ◽  
Personal Connections

AbstractGroundwater-level monitoring provides crucial information on the nature and status of aquifers and their response to stressors like climate change, groundwater extraction, and land use changes. Therefore, the development of a spatially distributed long-term monitoring network is indispensable for sustainable groundwater resource management. Despite being one of our greatest unseen resources, groundwater systems are too often poorly understood, ineffectively managed, and unsustainably used. This study investigates the feasibility of establishing a groundwater monitoring network mobilizing citizen scientists. We established a network of 45 shallow monitoring wells in the Kathmandu Valley using existing wells. We recruited 75% of the citizen scientists through personal connections and the rest through outreach programs at academic institutes and site visits. We used various methods to encourage citizen scientists to complete regular measurements and solicited feedback from them based on their experiences. Citizen scientists were more consistent during the monsoon season (June through September) than non-monsoon seasons. The depth-to-water below the ground surface varied from − 0.11 m (negative sign represents a groundwater level higher than the ground surface) to 11.5 m, with a mean of 4.07 m and standard deviation of 2.63 m. Groundwater levels began to rise abruptly with the onset of monsoon season and the shallowest and the deepest groundwater levels were recorded in peak rainfall months and dry months respectively. Citizen science-based groundwater monitoring using existing wells would be an economic and sustainable approach for groundwater monitoring. Improved groundwater-level data will provide essential information for understanding the shallow groundwater system of the valley, which will assist concerned authorities in planning and formulating evidence-based policy on sustainable groundwater management.

scholarly journals Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm

2021 ◽  
Vol 22 (1) ◽  
pp. 385
Author(s):  
Said Tkatek ◽  
Saadia Bahti ◽  
Otman Abdoun ◽  
Jaafar Abouchabaka
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Support ◽  
Intelligent System ◽  
Decision Support Tool ◽  
Good Decision ◽  
Support Tool ◽  
Hr Managers ◽  
The Right ◽  
Recruitment Model

<p>The human resources (HR) manager needs effective tools to be able to move away from traditional recruitment processes to make the good decision to select the good candidates for the good posts. To do this, we deliver an intelligent recruitment decision-making method for HR, incorporating a recruitment model based on the multipack model known as the NP-hard model. The system, which is a decision support tool, often integrates a genetic approach that operates alternately in parallel and sequentially. This approach will provide the best recruiting solution to allow HR managers to make the right decision to ensure the best possible compatibility with the desired objectives. Operationally, this system can also predict the altered choice of parallel genetic algorithm (PGA) or sequential genetic algorithm (SeqGA) depending on the size of the instance and constraints of the recruiting posts to produce the quality solution in a reduced CPU time for recruiting decision-making. The results obtained in various tests confirm the performance of this intelligent system which can be used as a decision support tool for intelligently optimized recruitment.</p>

scholarly journals Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors

2019 ◽  
Author(s):  
Christian Lehr ◽  
Gunnar Lischeid
Keyword(s):  
Groundwater Level ◽  
Measurement Errors ◽  
Monitoring Network ◽  
Anthropogenic Influences ◽  
Monitoring Networks ◽  
Normal Behaviour ◽  
Technical Problems ◽  
Groundwater Head ◽  
Efficient Screening ◽  
Selection Of

Abstract. Groundwater level data is monitored by environmental agencies to support sustainable use of groundwater resources. For this purpose a high spatial coverage of the monitoring networks and continuous monitoring in high temporal resolution is desired. This leads to large data sets that have to be quality checked and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater head all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected normal behaviour at the respective well as it is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the stable principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of analysed observation wells, respectively complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow to quality check the data for measurement errors and identify wells with possible anthropogenic influence. The approach was tested with 141 groundwater head series of the state authority groundwater monitoring network in northeast Germany covering the period from 1993 to 2013 in approximately weekly resolution.

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