Optimal Monitoring Network Research Articles

An Entropy-Based Approach to Fuzzy Multi-objective Optimization of Reservoir Water Quality Monitoring Networks Considering Uncertainties

In this study, a new fuzzy methodology for a multi-objective optimization of reservoir Water Quality Monitoring Stations (WQMS) was developed, based on Transinformation Entropy (TE), the IRanian Water Quality Index (IRWQI), and fuzzy social choice considering uncertainties. The approach was utilized in the Karkheh Dam reservoir in Iran. The objective functions were: 1) minimizing costs, 2) minimizing redundant information and uncertainties, and 3) maximizing the spatial coverage of the network. A CE-QUAL-W2 model was used for the simulation of water quality variables. The IRWQI was computed to reveal a complete picture of the reservoir water quality. The TE quantities were calculated for each pair of potential stations. The TE values were plotted against the spatial distances among potential WQMS to obtain the TE–Distance (TE–D) curve, and minimize redundant information among stations, while providing coverage of the entire network. A multi-objective Genetic Algorithm (NSGA-II) was applied to obtain Pareto-optimal solutions taking stakeholder preference into account. The most preferred solution was then obtained using fuzzy social choice approaches to achieve a consensus. The fuzziness embedded in the decision-making procedure, the uncertainty in the value of mutual information, and the uncertainty in identifying the optimal distance among WQMS were also investigated. Results indicated that the three fuzzy social choice approaches (Borda Count, Minimax, and Approval Voting) led to the same number of optimized WQMS in each fuzzy alpha-cut. Based on the fuzzy linguistic quantifiers method, the number of optimized WQMS was increased.

Efficiency of species survey networks can be improved by integrating different monitoring approaches in a spatial prioritization design

AbstractPublic participation to monitoring programs is increasingly advocated to overcome scarcity of resources and deliver important information for policy‐making. Here, we illustrate the design of optimal monitoring networks for bird species of conservation concern in Catalonia (NE Spain), under different scenarios of combined governmental and citizen‐science monitoring approaches. In our case study, current government efforts, limited to protected areas, were insufficient to cover the whole spectrum of target species and species‐threat levels, reinforcing the assumption that citizen‐science data can greatly assist in achieving monitoring targets. However, simply carrying out both government and citizen‐science monitoringad hocled to inefficiency and duplication of efforts: some species were represented in excess of targets while several features were undersampled. Policy‐making should concentrate on providing an adequate platform for coordination of government and public‐participatory monitoring to minimize duplicated efforts, overcome the biases of each monitoring program and obtain the best from both.

Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach

Management of groundwater requires a sufficient coverage of accurate groundwater quality data. These data are usually collected from monitoring wells which are spatially distributed in the river basin or the groundwater body that is studied. A minimum number of monitoring wells with an optimum spatial distribution is desired to ensure a cost-effective observation of the groundwater body. Therefore, the configuration of groundwater monitoring networks and the number of required wells becomes an important engineering optimization problem. The goal of this study is to find an optimum monitoring network with the fewest wells that provides sufficient spatial coverage on groundwater quality. With the presented method redundant wells in an already existing network are identified. Here, a genetic algorithm (GA) based optimization approach is used in which each monitoring well in the watershed is represented with a binary GA bit to evaluate if the corresponding monitoring well will be selected for the network. The proposed approach can solve the problem by simultaneously optimizing two conflicting objectives. The first objective is the maximization of the match between the interpolated groundwater quality concentration distributions obtained using data from all available monitoring wells and the wells from the newly generated network. The match is primarily evaluated using the Nash-Sutcliffe (NS) model efficiency. Groundwater quality is represented by the water quality index WQI that aggregates several quality parameters. The second objective deals with the minimization of the number of monitoring wells in the newly generated network by considering cost-related constraints. These two objectives are integrated in a single objective function where different combinations of both objectives are investigated by considering two cases. The applicability of the proposed approach is evaluated for the groundwater monitoring network of the Gediz River Basin (GRB) which is one of the most important river basins in Turkey. Findings indicate that the proposed approach significantly reduces the number of monitoring wells with a relatively small deviation of the spatial distribution of the WQI values. Also, the monitoring network is optimized such that sampling points are removed from less polluted areas and selected in areas with higher pollutant concentrations.

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

Snow has a unique characteristic in the water cycle, that is, snow falls during the entire winter season, but the discharge from snowmelt is typically delayed until the melting period and occurs in a relatively short period. Therefore, reliable observations from an optimal snow monitoring network are necessary for an efficient management of snowmelt water for flood prevention and hydropower generation. The Dual Entropy and Multiobjective Optimization is applied to design snow monitoring networks in La Grande River Basin in Québec and Columbia River Basin in British Columbia. While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements. First, several data quantization cases and their effects on the snow network design problems were explored. Second, the applicability the Snow Data Assimilation System (SNODAS) products as synthetic datasets of potential stations was demonstrated in the design of the snow monitoring network of the Columbia River Basin. Third, beyond finding the Pareto-optimal networks from the entropy with multi-objective optimization, the networks obtained for La Grande River Basin were further evaluated by applying three hydrologic models. The calibrated hydrologic models simulated discharges using the updated snow water equivalent data from the Pareto-optimal networks. Then, the model performances for high flows were compared to determine the best optimal network for enhanced spring runoff forecasting.

Optimization of deformation monitoring networks using finite element strain analysis

Abstract An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point.

Reconnecting Stochastic Methods With Hydrogeological Applications: A Utilitarian Uncertainty Analysis and Risk Assessment Approach for the Design of Optimal Monitoring Networks

AbstractCollaboration between academics and practitioners promotes knowledge transfer between research and industry, with both sides benefiting greatly. However, academic approaches are often not feasible given real‐world limits on time, cost and data availability, especially for risk and uncertainty analyses. Although the need for uncertainty quantification and risk assessment are clear, there are few published studies examining how scientific methods can be used in practice. In this work, we introduce possible strategies for transferring and communicating academic approaches to real‐world applications, countering the current disconnect between increasingly sophisticated academic methods and methods that work and are accepted in practice. We analyze a collaboration between academics and water suppliers in Germany who wanted to design optimal groundwater monitoring networks for drinking‐water well catchments. Our key conclusions are: to prefer multiobjective over single‐objective optimization; to replace Monte‐Carlo analyses by scenario methods; and to replace data‐hungry quantitative risk assessment by easy‐to‐communicate qualitative methods. For improved communication, it is critical to set up common glossaries of terms to avoid misunderstandings, use striking visualization to communicate key concepts, and jointly and continually revisit the project objectives. Ultimately, these approaches and recommendations are simple and utilitarian enough to be transferred directly to other practical water resource related problems.

Optimum Water Quality Monitoring Network Design for Bidirectional River Systems.

Affected by regular tides, bidirectional water flows play a crucial role in surface river systems. Using optimization theory to design a water quality monitoring network can reduce the redundant monitoring nodes as well as save the costs for building and running a monitoring network. A novel algorithm is proposed to design an optimum water quality monitoring network for tidal rivers with bidirectional water flows. Two optimization objectives of minimum pollution detection time and maximum pollution detection probability are used in our optimization algorithm. We modify the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and develop new fitness functions to calculate pollution detection time and pollution detection probability in a discrete manner. In addition, the Storm Water Management Model (SWMM) is used to simulate hydraulic characteristics and pollution events based on a hypothetical river system studied in the literature. Experimental results show that our algorithm can obtain a better Pareto frontier. The influence of bidirectional water flows to the network design is also identified, which has not been studied in the literature. Besides that, we also find that the probability of bidirectional water flows has no effect on the optimum monitoring network design but slightly changes the mean pollution detection time.

Optimal spatio-temporal design of water quality monitoring networks for reservoirs: Application of the concept of value of information

This paper presents a new methodology for optimizing Water Quality Monitoring (WQM) networks of reservoirs and lakes using the concept of the value of information (VOI) and utilizing results of a calibrated numerical water quality simulation model. With reference to the value of information theory, water quality of every checkpoint with a specific prior probability differs in time. After analyzing water quality samples taken from potential monitoring points, the posterior probabilities are updated using the Baye's theorem, and VOI of the samples is calculated. In the next step, the stations with maximum VOI is selected as optimal stations. This process is repeated for each sampling interval to obtain optimal monitoring network locations for each interval. The results of the proposed VOI-based methodology is compared with those obtained using an entropy theoretic approach. As the results of the two methodologies would be partially different, in the next step, the results are combined using a weighting method. Finally, the optimal sampling interval and location of WQM stations are chosen using the Evidential Reasoning (ER) decision making method. The efficiency and applicability of the methodology are evaluated using available water quantity and quality data of the Karkheh Reservoir in the southwestern part of Iran.

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting–California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

A geostatistical protocol to optimize spatial sampling of domestic drinking water supplies in remote environments

This paper deals with the design of optimal spatial sampling of water quality variables in remote regions, where logistics are complicated and the optimization of monitoring networks may be critical to maximize the effectiveness of human and material resources. A methodology that combines the probability of exceeding some particular thresholds with a measurement of the information provided by each pair of experimental points has been introduced. This network optimization concept, where the basic unit of information is not a single spatial location but a pair of spatial locations, is used to emphasize the locations with the greatest information, which are those at the border of the phenomenon (for example contamination or a quality variable exceeding a given threshold), that is, where the variable at one of the locations in the pair is above the threshold value and the other is below the threshold. The methodology is illustrated with a case of optimizing the monitoring network by optimal selection of the subset that best describes the information provided by an exhaustive survey done at a given moment in time but which cannot be repeated systematically due to time or economic constrains.

Optimization of a Water Quality Monitoring Network Using a Spatially Referenced Water Quality Model and a Genetic Algorithm

The monitoring network for a river system is designed to provide information about water quantity and quality. The development of Watershed Protection Plans and Total Maximum Daily Loads require systematic monitoring of waterbodies. In this study, optimum water quality monitoring networks were selected to assess E. coli loads in the Guadalupe River and San Antonio River basins. A Genetic Algorithm (GA) was applied to select monitoring stations using the mean annual E. coli flux from the Spatially Referenced Regression Model on Watershed Attributes (SPARROW). The objectives of the GA were to minimize the number of monitoring stations, include large values of the mean annual E. coli flux, and minimize uncertainty of the flux estimations. Constraints related to the monitoring of critical locations were included in a multi-objective optimization problem. The SPARROW model was applied to the optimized GA solution sets, which were compared using the objective values and statistical indices. The best GA-generated alternative set adequately represented the San Antonio River basin, in good agreement with a previous study conducted using only SPARROW. The application of the GA ensured the inclusion of the monitoring stations with large values of E. coli flux, which reflected high-risk areas within the watershed.

Spatial prediction of water quality variables along a main river channel, in presence of pollution hotspots

In order to treat and evaluate the available data of water quality and fully exploit monitoring results (e.g. characterize regional patterns, optimize monitoring networks, infer conditions at unmonitored locations, etc.), it is crucial to develop improved and efficient methodologies. Accordingly, estimation of water quality along fluvial ecosystems is a frequent task in environment studies. In this work, a particular case of this problem is examined, namely, the estimation of water quality along a main stem of a large basin (where most anthropic activity takes place), from observational data measured along this river channel. We adapted topological kriging to this case, where each watershed contains all the watersheds of the upstream observed data (“nested support effect”). Data analysis was additionally extended by taking into account the upstream distance to the closest contamination hotspot as an external drift. We propose choosing the best estimation method by cross-validation. The methodological approach in spatial variability modeling may be used for optimizing the water quality monitoring of a given watercourse. The methodology presented is applied to 28 water quality variables measured along the Santiago River in Western Mexico.

Optimization of hydrometric monitoring network in urban drainage systems using information theory.

Regular and continuous monitoring of urban runoff in both quality and quantity aspects is of great importance for controlling and managing surface runoff. Due to the considerable costs of establishing new gauges, optimization of the monitoring network is essential. This research proposes an approach for site selection of new discharge stations in urban areas, based on entropy theory in conjunction with multi-objective optimization tools and numerical models. The modeling framework provides an optimal trade-off between the maximum possible information content and the minimum shared information among stations. This approach was applied to the main surface-water collection system in Tehran to determine new optimal monitoring points under the cost considerations. Experimental results on this drainage network show that the obtained cost-effective designs noticeably outperform the consulting engineers' proposal in terms of both information contents and shared information. The research also determined the highly frequent sites at the Pareto front which might be important for decision makers to give a priority for gauge installation on those locations of the network.

Erratum to: Design of optimal ecosystem monitoring networks: hotspot detection and biodiversity patterns

Erratum to: Design of optimal ecosystem monitoring networks: hotspot detection and biodiversity patterns

Optimization of groundwater level monitoring network using GIS-based geostatistical method and multi-parameter analysis: A case study in Wainganga Sub-basin, India

Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation (GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology (LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System (GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.

An efficient methodology to design optimal groundwater level monitoring network in Al-Buraimi region, Oman

Designing an optimal groundwater level monitoring network (GLMN) is a major challenge and one of the primary goals of groundwater management. This paper proposes a methodology that incorporates comparative analysis of IDW, spline, ordinary kriging (OKrig), and empirical Bayesian kriging (EBK) interpolation methods with “1-fold cross-validation” technique for the regionalization and redesigning of the existing GLMN in an arid hardrock-alluvium Al-Buraimi region, Oman-UAE border. The performance indicators (weighted RMSE = 10.84 m and weighted R2 = 0.79) show the superiority of the EBK interpolation method over other methods and reveal reasonably accurate results when applied in areas with sparse and scarce observation wells. A new GLMN is proposed with regard to the results of the EBK method in which the idea of a “secondary observation network” is presented to reduce time and cost needed for groundwater level measurements. The proposed GLMN consists of 14 new observation wells added to 39 existing wells in order to provide better quality data compared to the current GLMN. The procedure used herein provides a practical and straightforward method to regionalize groundwater level variations and redesign the GLMN in regions with complex hardrock-alluvium geological setting.

Saltwater intrusion processes in coastal aquifers – modelling and management: a review

Saltwater intrusion in coastal aquifers due to human intervention has become a major challenge in different parts of the world. This review highlights some of the factors influencing saltwater intrusion processes in coastal aquifers, and discusses the methodologies proposed for development of management strategies on a regional scale, utilizing solutions of mathematical models. In recent years, significant advancements have been made in the development of mathematical tools for evolving optimal management strategies aimed at controlling saltwater intrusion in coastal aquifers. This article also reviews different modelling techniques utilized, and offers a brief account of management strategies prescribed by several researchers. Two types of modelling approaches exist in the literature: descriptive models aiming at simulating the physical processes analytically or numerically for evaluating the impact of a chosen management strategy, and prescriptive models for choosing optimal aquifer management strategies. Recent trend is to develop artificial-intelligence-based surrogate models trained by using random inputs and corresponding numerical outputs in order to develop coastal aquifer management strategy. Therefore, different surrogate modelling approaches aimed at reducing the computational burden in a linked simulation-optimization methodology are also highlighted in this review. Furthermore, this review provides a brief outline of the development of methodologies for optimal monitoring network design and sequential compliance monitoring of the real life consequences of implementing prescribed management strategies for coastal aquifers.

Stakeholder engagement in multi-objective optimization of water quality monitoring network, case study: Karkheh Dam reservoir

Reservoir water quality is important for water quality management downstream. A hierarchical approach is developed to present the monitoring locations within a format that satisfies the objectives of social stakeholders for making final decisions. First, a CE-QUAL-W2 model is applied to simulate water quality variables in the reservoir for a long time using a set of historic data. Second, transinformation entropy theory is used to quantify mutual information among a set of monitoring stations for each water quality variable. Then, a non-dominating sorting genetic algorithm-based model is developed for multi-objective optimization of the water quality monitoring network. Finally, a social choice method is applied to the identified non-dominated solutions to achieve a strategy that is compromised among stakeholders. The variations of the water quality variables at different depths and different seasons are investigated. The proposed approach is illustrated for Karkheh Reservoir in Iran. The number of optimized monitoring stations is the same for all seasons (three out of 22 potential stations) using different social choice methods. The results show the appropriate performance of the proposed methodology for optimization of reservoir water quality monitoring stations.

Hydrometric network design using dual entropy multi-objective optimization in the Ottawa River Basin

Abstract Water resources managers commonly rely on information collected by hydrometric networks without clear knowledge of their efficiency. Optimal water monitoring networks are still scarce especially in the Canadian context. Herein, a dual entropy multi-objective optimization (DEMO) method uses information theory to identify locations where the addition of a hydrometric station would optimally complement the information content of an existing network. This research explores the utility of transinformation (TI) analysis, which can quantitatively measure the contribution of unique information from a hydrometric station. When used in conjunction, these methods provide an objective measure of network efficiency, and allow the user to make recommendations to improve existing hydrometric networks. A technique for identifying and dealing with regulated basins and their related bias on streamflow regionalization is also examined. The Ottawa River Basin, a large Canadian watershed with a number of regulated hydroelectric dams, was selected for the experiment. The TI analysis approach provides preliminary information which is supported by DEMO results. Regionalization was shown to be more accurate when the regulated basin stations were omitted using leave one out cross validation. DEMO analysis was performed with these improvements and successfully identified optimal locations for new hydrometric stations in the Ottawa River Basin.

Optimization of Groundwater Quality Monitoring Network Using Risk Assessment and Geostatistic Approach

The sampling data from the groundwater quality monitoring network (GQMN) can not only characterize the properties of the natural resource in groundwater system, but also delineate contaminated area and risk potential by human activities. This study aimed to provide a process for designing GQMN for non-existing monitoring well using Nantou area in Taiwan as the example. First, this study have integrated four contributing factors (land use, soil media, topography and population) of both vulnerability map and hazard map to calculate the Contamination Risk Index (CRI) via Geographic Information System (GIS) model. The results of the map demonstrated that the spatial distribution of the highest contamination risk potential and provided the preliminary deployment of the spatial locations of the candidate monitoring wells. According to the deployment of the candidate monitoring wells, this study used the geostatistical approach to calculate the percentage of the total area with acceptance accuracy of the candidate monitoring wells for different threshold. Based on the defined performance, the available monitoring networks for different threshold of three cases are estimated. The information can support the designer or the decision maker to design appropriate monitoring network whether they have a limited funding or not.