Groundwater Quality Simulation Research Articles

Simulation of Groundwater Quality in Xi 'an,Shaanxi Province Based on Visual Modflow

The so-called groundwater quality simulation is to study the spatial and temporal variation law of the concentration of a certain soluble substance (element or other component) when it is dispersed and transported by simulation method, to predict the instantaneous dynamics and expansion range of polluted groundwater, and to provide scientific basis for formulating reasonable and effective prevention measures. In recent years, the environmental hydrogeology survey results of some provinces, cities and regions in China show that the groundwater in most cities in China has been polluted to varying degrees. This situation directly affects industrial and agricultural production and people's health. However, in the past, environmental hydrogeology work was mostly limited to surface investigation, lacking quantitative and predictive evaluation research. Monitoring groundwater quality dynamics and forecasting the development trend of groundwater pollution is one of the main tasks of groundwater pollution investigation and research. Relevant information shows that some countries in the world have already carried out this research and established a predictive groundwater quality model. Among them, the United States has established 22 material transport models and 5 heat transport models; France has established 5 models of material transport and 4 models of heat transport. The following countries have only established material transport models: 7 in Israel; 2 in the UK; 2 in Canada; 1 in West Germany and 1 in Japan. Therefore, we believe that the study of groundwater quality simulation experiment, which is still blank in China's scientific field, has become an important subject that needs to be carried out urgently to ensure the four modernizations and people's health. The hydrogeological conditions and dynamic characteristics of groundwater resources have been comprehensively analyzed and studied. It points out that the continuous exploitation of groundwater resources in 2018 has led to the deterioration of groundwater quality, land degradation, underground, cracks and depletion of groundwater resources, and puts forward some preventive measures.

Simulation of ground water quality for noyyal river basin of Coimbatore city, Tamilnadu using MODFLOW

In recent years groundwater modelling has become a major part of many projects dealing with groundwater exploitation, protection and remediation. Coimbatore city is located along the Noyyal River which is grown in size and population on either side of the river and the dumping of wastes very close to the river banks, leads to pollution of both surface and groundwater. Study on the quality of groundwater along the Noyyal River in Coimbatore city of Tamilnadu, is necessary to safeguard the interest of the people with respect to quality and quantity of water. The objective of the research is to develop Groundwater quality models to suggest ways and means to contain and remediate the polluted groundwater under various conditions. Data related to the groundwater quality, rainfall and well log were collected from the reputed government departments and fifteen sample well locations are identified near the noyyal river basin. The quality parameters such as pH, TDS, EC, TH, chlorides, alkalinity, sodium, calcium, magnesium, sulphates and nitrates are considered for the study. Groundwater quality modelling is attempted by using MODFLOW/MT3DMS with different scenarios. This study revealed that in all the scenarios the center portion of study area is more affected i.e. from P·N.Palayam to Kalangal. The groundwater flow is moving towards and along the river flow. Hence the pollutants are moving easily from upstream to the downstream side. necessary measures has to be taken to control the groundwater contamination.

Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence

Water quality experiments are difficult, costly, and time-consuming. Therefore, different modeling methods can be used as an alternative for these experiments. To achieve the research objective, geospatial artificial intelligence approaches such as the self-organizing map (SOM), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) were used to simulate groundwater quality in the Mazandaran plain in the north of Iran. Geographical information system (GIS) techniques were used as a pre-processer and post-processer. Data from 85 drinking water wells was used as secondary data and were separated into two splits of (a) 70 percent for training (60% for training and 10% for cross-validation), and (b) 30 percent for the test stage. The groundwater quality index (GWQI) and the effective water quality factors (distance from industries, groundwater depth, and transmissivity of aquifer formations) were implemented as output and input variables, respectively. Statistical indices (i.e., R squared (R-sqr) and the mean squared error (MSE)) were utilized to compare the performance of three methods. The results demonstrate the high performance of the three methods in groundwater quality simulation. However, in the test stage, CANFIS (R-sqr = 0.89) had a higher performance than the SOM (R-sqr = 0.8) and ANN (R-sqr = 0.73) methods. The tested CANFIS model was used to estimate GWQI values on the area of the plain. Finally, the groundwater quality was mapped in a GIS environment associated with CANFIS simulation. The results can be used to manage groundwater quality as well as support and contribute to the sustainable development goal (SDG)-6, SDG-11, and SDG-13.

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

Groundwater quality modeling using self-organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts

Groundwater is the main source for water provision in the arid and semi-arid areas such as Iran. The groundwater quality was simulated by using a hybrid model integrating a Self-Organizing Map (SOM) and geographic information system (GIS). SOM and GIS were used as pre-processing and post-processing tools in the Mazandaran Plain. Further, the Ground Water Quality Index (GWQI) and its effective factors were estimated by using digital maps and the secondary data. NeuroSolutions software was used for simulating the groundwater quality. To do this, a model was trained and optimized in the SOM and then the optimized model was tested. In the next step, the performance of SOM in groundwater quality simulation was confirmed (test stage, Rsqr=0.8, and MSE=0.008). Then, the digital maps of the SOM inputs were converted to raster format in GIS. In the last step, a raster layer was generated by combining the model input layers which comprised the model inputs values. The tested SOM was used to simulate GWQI in the sites without the secondary data of the groundwater quality. Finally, the groundwater quality map was generated by coupling the results of SOM estimations and GIS capabilities. The results revealed that the coupling of SOM and GIS has high performance in the simulation of the groundwater quality. According to the results, a limited area of the studied plain has groundwater resources with low quality (GWQI>0.04). Therefore, that will be a threat to the life of humans, animals, and vegetative species. Therefore, it is necessary to plan for managing the groundwater quality in the Mazandaran plain.

Modeling groundwater quality by using hybrid intelligent and geostatistical methods.

Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (μmho/cm), 444.152 (μmho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study’s results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.