Using neural network modeling to improve the detection accuracy of land subsidence due to groundwater withdrawal

Despite the high efficiency of remote sensing methods for rapid and large-scale detection of subsidence phenomena,this technique has limitations such as atmospheric impact and temporal and spatial decorrelation that affect the accuracy of the results.This paper proposes a method based on an artificial neural network to improve the results of monitoring land subsidence due to groundwater overexploitation by radar interferometry in the Aliabad plain(Central Iran).In this regard,vertical ground deformations were monitored over 18 months using the Sentinel-1A SAR images.To model the land subsidence by a multilayer perceptron(MLP)artificial neural network,four parameters,including groundwater level,alluvial thickness,elastic modulus,and transmissivity have been applied.The model's generalizability was assessed using data derived for 144 days.According to the results,the neural network estimates the land subsidence at each ground point with an accuracy of 6.8 mm.A comparison between the predicted and actual values indicated a significant agreement.The MLP model can be used to improve the results of subsidence detection in the study area or other areas with similar characteristics.

Well-Drilling and Groundwater Monitoring Network Construction:Taking Changde City as an Example

Entrusted by the Environmental Protection Bureau of Changde City,we conducted drilling,sampling survey and constructed a monitoring network for groundwater in several counties and districts of Changde City.This article introduces the drilling technology,detection method and detection network layout plan adopted in the project,and expounds the problems that occurred while executing the project,in order to provide reference for similar groundwater capacity supervision and construction projects.

Groundwater recharge via precipitation in the Badain Jaran Desert,China

Precipitation infiltration serves as a significant source of groundwater in the Badain Jaran Desert.To investigate variations in precipitation infiltration within the desert,this study collected data on moisture content and temperature from the vadose zone through in-situ field monitoring.Utilizing these data,a numerical model is employed to explore the mechanism of groundwater recharge via precipitation.The results are as follows:(1)Moisture content and temperature in the shallow vadose zone exhibit significant seasonal variations,with moisture content diminishing with increasing depth;(2)Groundwater recharge via precipitation infiltration initially increases and then decreases with groundwater level depth(GWD).Peak groundwater recharge via precipitation occurs at a GWD of 0.75 m,decreasing to merely 0.012 cm at GWDs exceeding 2 m;(3)Groundwater is no longer susceptible to phreatic water evaporation when the GWD reaches approximately 3.7 m.Therefore,GWD plays a crucial role in governing groundwater recharge via precipitation in the Badain Jaran Desert.

Developing three-dimensional groundwater flow modeling for the Erbil Basin using Groundwater Modeling System (GMS)

This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Iraq,is a vital water resource area facing increasing water demands and environ-mental challenges.The three-dimensional nature of the groundwater flow system is crucial for accurately understanding and managing water resources in the basin.The modeling process involved data collection,geological and hydrogeological characterization,conceptual model development,and numerical simulation using GMS software MODFLOW 2000 package.Various parameters such as hydraulic conductivity,recharge rates,and boundary conditions were integrated into the model to represent the complex hydrogeo-logical conditions of the basin.Model calibration was performed by comparing simulated groundwater levels with observed data from monitoring wells across the basin,using the automatic calibration method of automated Parameter Estimation(PEST).Pilot points were applied to adjust the hydraulic conductivity in the model area spatially.Sensitivity analysis was conducted to assess the influence of key parameters on model predictions and to identify areas of uncertainty.The developed three-dimensional groundwater flow model provides valuable insights into the dynamics of groundwater flow,recharge-discharge mechanisms,and potential impacts of future scenarios such as climate change and water resource management strategies.It serves as a useful tool for decision-makers,water resource managers,and researchers to evaluate differ-ent management scenarios and formulate sustainable groundwater management policies for the Erbil Basin.In conclusion,this study demonstrates the effectiveness of using GMS for developing three-dimensional groundwater flow models in complex hydrogeological settings like the Erbil Basin,contributing to improved understanding and management of groundwater resources in the region.

Upgrading a regional groundwater level monitoring network for Beijing Plain,China

Monitoring of regional groundwater levels provides important information for quantifying groundwater depletion and assessing impacts on the environment. Historically, groundwater level monitoring wells in Beijing Plain, China, were installed for assessing groundwater resources and for monitoring the cone of depression. Monitoring wells are clustered around well fields and urban areas. There is urgent need to upgrade the existing monitoring wells to a regional groundwater level monitoring network to acquire information for integrated water resources management. A new method was proposed for designing a regional groundwater level monitoring network. The method is based on groundwater regime zone mapping. Groundwater regime zone map delineates distinct areas of possible different groundwater level variations and is useful for locating groundwater monitoring wells. This method was applied to Beijing Plain to upgrade a regional groundwater level monitoring network.

Identification of Contaminant Source Characteristics and Monitoring Network Design in Groundwater Aquifers: An Overview

The groundwater system is often polluted by different sources of contamination where the sources are difficult to detect. The presence of contamination in groundwater poses significant challenges to its delineation and quantification. The remediation of a contaminated site requires an optimal decision making system to identify the pollutant source characteristics accurately and efficiently. The source characteristics are generally identified using contaminant concentration measurements from arbitrary or planned monitoring locations. To effectively characterize the sources of pollution, the monitoring locations should be selected appropriately. An efficient monitoring network will result in satisfactory characterization of contaminant sources. On the other hand, an appropriate design of monitoring network requires reliable source characteristics. A coupled iterative sequential source identification and dynamic monitoring network design, improves substantially the accuracy of source identification model. This paper reviews different source identification and monitoring network design methods in groundwater contaminant sites. Further, the models for sequential integration of these two models are presented. The effective integration of source identification and dedicated monitoring network design models, distributed sources, parameter uncertainty, and pollutant geo-chemistry are some of the issues which need to be addressed in efficient, accurate and widely applicable methodologies for identification of unknown pollutant sources in contaminated aquifers.

Entropy-Based Approach to Remove Redundant Monitoring Wells from Regional-Scale Groundwater Network

An entropy-based approach is applied to identify redundant wells in thenetwork. In the process of this research, groundwater-monitoring network is considered as acommunication system with a capability to transfer information, and monitoring wells are taken asinformation receivers. The concepts of entropy and mutual information are then applied to measurethe information content of individual monitoring well and information relationship betweenmonitoring well pairs. The efficiency of information transfer among monitoring wells is the basis tojudge the redundancy in the network. And the capacity of the monitoring wells to provideinformation on groundwater is the point of evaluation to identify redundant monitoring wells. Thisapproach is demonstrated using the data from a regional-scale ground-water network in Hebei plain,China The result shows that the entropy-based method is recommendable in optimizing groundwaternetworks, especially for those within media of higher heterogeneities and ani-sotropies.

The Application of Remote Sensing Monitoring Method and Groundwater Model for Water Rights Management in Haihe River basin

Groundwater is an important water resource in Haihe River basin,North China.The number of aquifers that appear to be declining under conditions of groundwater overdraft is increasing.To effectively manage the water resources,there is a strong scientific need to analyze the net use of this important water resource and to quantify the water rights allocation for improved understanding of the future water

Groundwater level monitoring-importance global groundwater monitoring network

The paper illustrates the concept, methodology, essential components and importance of groundwater level monitoring in terms of various aquifers such as multiple aquifer, karst aquifer and other aquifers. The groundwater resources in Mekong countries including Cambodia, Laos PDR, Myanmar, Thailand and Vietnam have also been reviewed. Finally, the author briefly presents Global Groundwater Monitoring Network.

Assessment of Groundwater Quality Monitoring Network Using Cluster Analysis, Shib-Kuh Plain, Shur Watershed, Iran

In addition to failure in monitoring water quality in the study area, the monitoring networks are not designed based on standard guidelines;moreover, there is no ongoing assessment of these networks. The great or small number of sites in the monitoring networks will cause problems. This paper aims to evaluate the monitoring networks of the changes in groundwater quality of the Shib-Kuh aquifer established in 2005 in South West of Iran. The aim of this study is to improve the monitoring networks and save expenses. In this aquifer, the groundwater main chemical anions, cations, EC, TDS, TH, SAR and pH are measured in 20 sites. The statistical cluster analysis methods are used and observations, variables and sampling sites are analyzed and evaluated. The results showed the probability of about 25 percent reduction in the sites. It also proved that it was possible for some of the measuring parameters to have been removed. Cluster analysis method is a suitable way to evaluate the quality of establishment as well as the function of the monitoring networks of water resources. Through the application of this method, the number of sites, variables, or both of these factors can be optimized and this optimization leads to upgrading of monitoring networks.

Integrated Sequential Groundwater Contaminant Source Characterization and Pareto-Optimal Monitoring Network Design Application for a Contaminated Aquifer Site

Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.

The Jinsha River Groundwater Observation Network and Discussion about Its Earthquake Precursor Monitoring Capability

A groundwater observational network for monitoring seismic precursors has been established at the reservoirs on the lower reaches of the Jinsha River in southwestern China, where a series of hydropower stations are under construction. It is the second network in China that is operated by enterprises with the purpose to observe and study earthquake precursors in reservoir areas. This paper presents the layout and technical constitution of the network, features of its observational wells and aquifers and the preliminary result of its experimental operation. Its capability to monitor seismic precursor is evaluated based on an analysis of the well-aquifer system as well as the multiple-monthly, monthly, daily and hourly variations of water levels and water temperatures observed by this network.

Distinguishing the main components of submarine groundwater and estimating the corresponding fluxes based on radium tracing method—taking the Maowei Sea for example

Submarine groundwater discharge(SGD)is an important part in the land-sea interactions,which mainly contains three components:submarine fresh groundwater discharge(SFGD),tidal flat recirculated saline groundwater discharge(tidal flat RSGD)and subtidal recirculated saline groundwater discharge(subtidal RSGD).In order to make a more accurate assessment of the impact of SGD on coastal ecological environment,it is necessary to distinguish the main components of SGD.In this study,the Maowei Sea,located in the northern part of the Beibu Gulf,was selected as the study area.Based on the radium(Ra)tracing method,we present a new analytical method for distinguishing the three main components of SGD in this area combined with field data.The average daily flow along the coastline of the Maowei Sea of tidal flat RSGD was slightly higher than that of SFGD,and both two were on the magnitude of 1×10^(5)m^(3)/d.The average daily flow for the subtidal RSGD of the entire subtidal zone of the Maowei Sea reached to the magnitude of 1×10^(6)-1×10^(7)m^(3)/d.The long-term variation trend of terrestrial SGD is a valuable information for the study of the influence of terrigenous material on the coastal ecological environment.Based on the results of four sampling periods,it is found that the fluxes of SFGD and tidal flat RSGD in the Maowei Sea had good linear correlation with the net precipitation.As an example,January 2015 to August 2022 were selected as the study periods,and the variation trends of SFGD and tidal flat RSGD were calculated by linear function with net precipitation as the independent variable.The results showed that the flux of tidal flat RSGD was slightly higher than that of SFGD,and the difference between the two is larger in flood season while smaller in dry season.In general,in the coastal range of China,the total SGD flux in the Maowei Sea area is at a high level,and the SFGD flux is at a medium level.

Identification of Groundwater Potential Zones in Samendeni Watershed in Sedimentary and Semi-Arid Contexts of Burkina Faso, Using Analytic Hierarchy Process (AHP) Method and GIS

Demand for water increases in Samendeni regarding the undertaken agricultural projects while pressure on surface water from global warming/evapotranspiration also increases. Thus, the need to evaluate the groundwater potential in the catchment is crucial as alternative supplier of water and resilience to climate hazards. The AHP was performed integrating ten influencing factors such as geomorphology, geology, soil, land use/land cover (lulc), slope, rainfall, drainage density, borehole rate & depth and piezometric level to generate groundwater potential zones (GWPZs) in Samendeni watershed (4420 km2). All the factors were processed and ranged into five (5) classes. Weight was assigned to each class of thematic layer. These thematic layers were then reclassified based on the normalized weight to be used in the calculation of groundwater potential zones (GWPZ). The final output, groundwater potential map, revealed a significant groundwater potential with very good (11%), good (31%), moderate (30%), poor (20%), and very poor (8%) of proportion. The interesting (very good, good) GWPZs in the study area are mostly in the central towards the east. The poor zones in term of groundwater potential are concentrated in the upper west region of the watershed. Besides the cross-validation with the relationship between different groundwater potential zones and the wells available in the study area, the overall accuracy was estimated to 88% provided from the result of the similarity analysis where 22 out of the 25 validation wells match with the expected yield classes of GWPZs. The statistics from that validation revealed the performance of AHP method to delineate groundwater potential zones at catchment level.

Natural water purification and water management by artificial groundwater recharge

Worldwide,several regions suffer from water scarcity and contamination.The infiltration and subsurface storage of rain and river water can reduce water stress.Artificial groundwater recharge,possibly combined with bank filtration,plant purification and/or the use of subsurface dams and artificial aquifers,is especially advantageous in areas where layers of gravel and sand exist below the earth's surface.Artificial infiltration of surface water into the uppermost aquifer has qualitative and quantitative advantages.The contamination of infiltrated river water will be reduced by natural attenuation.Clay minerals,iron hydroxide and humic matter as well as microorganisms located in the subsurface have high decontamination capacities.By this,a final water treatment,if necessary,becomes much easier and cheaper.The quantitative effect concerns the seasonally changing river discharge that influences the possibility of water extraction for drinking water purposes.Such changes can be equalised by seasonally adapted infiltration/extraction of water in/out of the aquifer according to the river discharge and the water need.This method enables a continuous water supply over the whole year.Generally,artificially recharged groundwater is better protected against pollution than surface water,and the delimitation of water protection zones makes it even more save.

Application of Artificial Neural Networks Model as Analytical Tool for Groundwater Salinity

The main source of water in Gaza Strip is the shallow coastal aquifer. It is extremely deteriorated in terms of salinity which influenced by many variables. Studying the relation between these variables and salinity is often a complex and nonlinear process, making it suitable to model by Artificial Neural Networks (ANN). Initially, it is assumed that the salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate, abstraction, abstraction average rate, life time and aquifer thickness. Data were extracted from 56 municipal wells, covering the area of Gaza Strip. After a number of modeling trials, the best neural network was determined to be Multilayer Perceptron network (MLP) with four layers: an input layer of 6 neurons, first hidden layer with 10 neurons, second hidden layer with 7 neurons and the output layer with 1 neuron which gives the final chloride concentration. The ANN model generated very good results depending on the high correlation between the observed and simulated values of chloride concentration. The correlation coefficient (r) was 0.9848. The high value of (r) showed that the simulated chloride concentration values using the ANN model were in very good agreement with the observed chloride concentration which mean that ANN model is useful and applicable for groundwater salinity modeling. ANN model was successfully utilized as analytical tool to study influence of the input variables on chloride concentration. It proved that chloride concentration in groundwater is reduced by decreasing abstraction, abstraction average rate and life time. Furthermore, it is reduced by increasing recharge rate and aquifer thickness.

Groundwater Level Prediction Using Artificial Neural Networks: A Case Study in Tra Noc Industrial Zone, Can Tho City, Vietnam

The objective of this study is to predict groundwater levels (GWLs) under different impact factors using Artificial Neural Network (ANN) for a case study in Tra Noc Industrial Zone, Can Tho City, Vietnam. This can be achieved by evaluating the current state of groundwater resources (GWR) exploitation, use and dynamics;setting-up, calibrating and validating the ANN;and then predicting GWLs at different lead times. The results show that GWLs in the study area have been found to reduce rapidly from 2000 to 2015, especially in the Middle-upper Pleistocene (qp2-3) and upper Pleistocene (qp3) due to the over-withdrawals from the enterprises for production purposes. Concerning this problem, an Official Letter of the People’s Committee of Can Tho City was issued and taken into enforcement in 2012 resulting in the reduction of exploitation. The calibrated ANN structures have successfully demonstrated that the GWLs can be predicted considering different impact factors. The predicted results will help to raise awareness and to draw an attention of the local/central government for a clear GWR management policy for the Mekong delta, especially the industrial zones in the urban areas such as Can Tho city.

Simulation and Prediction for Groundwater Dynamics Based on RBF Neural Network

Based on MATLAB, a new model-BRF network model is founded to be used in groundwater dynamic simulation and prediction. It is systematically studied about the training sample set, testing sample set, the pretreatment of the original data, neural network construction, training, testing and evaluating the entire process. A favorable result is achieved by applying the model to simulate and predict groundwater dynamics, which shows this new method is precise and scientific.

Groundwater Monitoring in the Gneisso-Basaltic Fractured Rock Aquiferous Formations of Kumba, Southwest Region Cameroon: Seasonal Variations in the Aqueous Geochemistry and Water Quality

The objective was to determine and monitor seasonal changes during four hydrological seasons: Wet season (September), Wetdry season (December), Dry season (March) and Drywet season (June) in the groundwater aqueous geochemistry and its domestic-agro-industrial quality using physicochemical parameters and hydrogeochemical tools: Temperature, Electrical Conductivity EC, pH, Total dissolved solids TDS, Ionic ratios, Gibbs diagrams, Piper diagrams Durov diagrams, total hardness HT, Water quality index WQI, Sodium adsorption ratio SAR, Percent Sodium %Na, Kelly’s Ratio KR, permeability index PI, Magnesium adsorption ratio MAR, Residual sodium carbonate RSC and Wilcox diagram. Field physicochemical parameters ranged from: Wet season;pH 3.9 - 6.9;Temperature, 23.3°C - 29.1°C;EC, 10 - 1900 μS/cm;TDS, 6.7 - 1273 mg/L;Wetdry, pH, 5.7 - 11.7;Temperature, 23.6°C - 28.3°C;EC, 1 - 1099 μS/cm, TDS, 0.67 - 736.33 mg/L;Dry pH, 5.7 - 13.1;Temperature, 26.3°C - 30.2°C;EC, 12 - 770 μS/cm, TDS, 8.04 - 515.9 mg/L and Drywet, pH, 4 - 7.4;Temperature, 25.8°C - 30.7°C;EC, 10 - 1220 μS/cm, TDS, 6.7 - 817.4 mg/L. Seventy-two groundwater samples, 18 per season were analysed. All ionic concentrations fell below acceptable World Health Organization guidelines in all seasons. The sequence of abundance of major ions are;Wet, Ca+ > Mg2+ > Na+ = K+ > NH4+, HCO3? > Cl? > NO3? > SO42? > HPO42?;Wetdry Ca+ > K+ > Mg2+ > Na+ > NH4+, HCO3? > Cl? > SO42? > NO3? > HPO42?;Dry Ca+ > K+ > Mg2+ > Na+ > NH4+, HCO3? > Cl?> NO3? > SO42? > HPO42?;Drywet NH4+ > Ca+ > K+ > Mg2+ > Na+;Cl? > HCO3? > NO3? > SO42? > HPO42?. Groundwater ionic content was due to rock weathering and ion exchange reactions. CaSO4 is the dominant water type in Wet and Wetdry seasons;followed by CaHCO3, Na + K-Cl Wet, CaSO4 and CaHCO3 Wetdry;MgCl Dry and Drywet followed by CaCl, CaHCO3 Dry and CaSO4, CaHCO3 Dry-Wet. The dominant hydrogeochemical facies are Ca-Mg-Cl-SO4 followed by Na-K-SO4 Wet and Ca-Mg-HCO3? in all other seasons. Ion exchange, Simple dissolution and uncommon dissolution are the processes determining groundwater character. The water quality indices;WQI, HT, SAR, %Na, KR, PI, MAR,RSC and Wilcox diagrams, indicate that groundwater in Kumba is 80% - 100% excellent during the Drywet &Wet seasons, 5% - 10% unsuitable during the Wetdry & Dry seasons for domestic use while being excellent-good for Agro-Industrial uses in all other seasons. Physicochemical parameters in some areas exceeded permissible limits for drinking. All hydrogeochemical parameters vary with seasons and these variations show the impact of annual cycles of seasonal changes on the aqueous geochemistry of groundwater in Kumba.