Optimal Groundwater Remediation Under Uncertainty Using Multi-objective Optimization

A methodology is developed for optimal remediation of groundwater aquifers under hydraulic conductivity uncertainty. A multi-objective management method based on a pump-and-treat remediation technology, is proposed. The pumping rates and well locations are the decision variables and two objectives are chosen: minimization of contaminated groundwater in the aquifer and minimization of remediation cost. A Monte Carlo simulation method is used to cope with hydraulic conductivity uncertainty. A number of equally probable realizations of hydraulic conductivity are created and a Pareto front is obtained using a modified multi-objective Genetic Algorithm. A penalty function is utilized to maintain the algebraic sum of pumping and recharging rates equal to zero. Since Monte Carlo simulations are CPU time consuming, a method is proposed to identify the few significant realizations which have an effect on the optimal solution (critical realizations). A Pareto front with an assigned probability is derived, so that the decision maker can make decisions with specified reliability. In a case study with 100 realizations, only 11 realizations were found critical and need be considered. The remaining 89 realizations consistently obtain low ranks for all designs considered and do not affect decisions at 95% reliability level. Thus these realizations need not be considered which implies a 89% savings in computer time. The designs obtained using the critical realizations, retain a similar reliability for new realizations not considered in the design process.     

Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach

Water Resources Management - Identifying areas with high groundwater potential is important for groundwater resources management. The main objective of this study is to propose a novel classifier...     

Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation

We herein propose a simulation-optimization model for groundwater remediation, using PAT (pump and treat), by coupling artificial neural network (ANN) with the grey wolf optimizer (GWO). The input and output datasets to train and validate the ANN model are generated by repetitively simulating the groundwater flow and solute transport processes using the analytic element method (AEM) and random walk particle tracking (RWPT). The input dataset is the different realization of the pumping strategy and output dataset are hydraulic head and contaminant concentration at predefined locations. The ANN model is used to approximate the flow and transport processes of two unconfined aquifer case studies. The performance evaluation of the ANN model showed that the value of mean squared error (MSE) is close to zero and the value of the correlation coefficient (R) is close to 0.99. These results certainly depict high accuracy of the ANN model in approximating the AEM-RWPT model. Further, the ANN model is coupled with the GWO and it is used for remediation design using PAT. A comparison of the results of the ANN-GWO model with solutions of ANN-PSO (ANN-Particle Swarm Optimization) and ANN-DE (ANN-Differential Evolution) models illustrates the better stability and convergence behaviour of the proposed methodology for groundwater remediation.

     

PRB技术在垃圾渗滤液污染地下水修复中的可行性研究

以被垃圾渗滤液污染的地下水为研究对象,分别用天然沸石、陶粒、活性炭、炉渣、零价铁作为反应介质,设计了5种地下可渗透反应器(PRB),分别为反应器A、B、C、D和E,对PRB技术治理污染地下水的可行性和有效性进行实验模拟研究.实验结果表明:反应器A～E对COD的去除率为82.36%～87.29%,NH4+的去除率较低,为12.91%～29.59%,说明PRB技术治理渗滤液污染的地下水有一定的可行性,但技术有待继续深入研究.     

纳米零价铁用于地下水污染修复时存在的问题与对策

介绍了纳米零价铁(Nanoscale Zero-Valent Iron,NZVI))用于地下水污染修复时存在的问题及改进技术。NZVI可以去除水体中的各种卤代烃(主要是氯代烃)以及Cr、Pb、As等重金属,但NZVI在水体中会由于团聚、沉淀和钝化等而降低其修复效率。将超声波技术和添加微生物方法与NZVI技术进行协同,以及对NZVI进行固体负载和表面改性是解决NZVI修复地下水时存在问题的有效途径。今后,探讨地下复杂环境因素对应用NZVI进行地下水修复的影响,以及改进NZVI水污染修复技术是利用NZVI修复地下水污染的重要研究方向。     

Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design

Obtaining optimal solutions for time-varying groundwater remediation design is a challenging task. A novel procedure first employs input/output data sets obtained by constrained differential dynamic programming (CDDP). Then the Adaptive-Network-Based Fuzzy Inference System (ANFIS), which is a fuzzy inference system (FIS) implemented in the adaptive network framework, is applied to acquire time-varying pumping rates. Results demonstrate that the FIS is an efficient way of groundwater remediation design.     

A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Today, various methods have been developed to extract drinking water resources, which scientists use to simulate the quantitative and qualitative water resources parameters. Due to Iran's geographical and climatic characteristics, this region is located on the drought belt in Asia. In this research, some Artificial Intelligence (AI) and mathematical models have been used for groundwater level prediction. The AI models used for this research are Extreme Learning Machine (ELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) model. In this study, simultaneously, these models were used to simulate and estimate groundwater level (GWL). The database used in the simulation is the data related to the Total Dissolved Solids (TDS), Electrical Conductivity (EC), Salinity (S), and Time (t) parameters. The results showed that ELM was more accurate than other methods. In Uncertainty Wilson Score Method (UWSM) analysis, ELM had an Underestimation performance and was determined as the more precise model.

     

Coupling Surface Water and Groundwater Flows in a Laboratory Model Using Foam as Artificial Groundwater Material

A laboratory mock-up of a surface water groundwater system with foam blocks used to replicate a groundwater material was mounted in a tidal basin in the Hydraulics Laboratory at Cardiff University, UK. River flow in the form of a channel was generated, with an oscillating weir at the lower boundary of the system producing tides or controlled water levels. The purpose of the experiment was to investigate the manner in which flow and a conservative tracer interacted between surface and sub-surface flows for simulated riverine and tidal conditions. In these controlled laboratory conditions, results obtained with respect to flows were consistent with the laws governing surface-subsurface flows. In contrast, the conservative tracer could not move through the foam for any meaningful results to be obtained, which was thought to be due to the dominance of surface tension and sorption in the foam pores.     

Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models, they are very easy to implement in karstic regions without the need of explicit knowledge of the exact flow conduit geometry and they avoid the creation of extremely complex models in the rare cases when all the necessary information is available. With hydrological parameters like rainfall and temperature, as well as with hydrogeological parameters like pumping rates from nearby wells as input, the ANN applies a black box approach and yields the simulated hydraulic head. During the calibration process the network is trained using a set of available field data and its performance is evaluated with a different set. Available measured data from Edward??s aquifer in Texas, USA are used in this work to train and evaluate the proposed ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2 m). That aside, the ANN is still a useful way to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models.     

水库防洪补源优化模式的探讨与实践

汛期将洪水经水库调蓄后以小流量下泄再回灌补给地下水，是多余洪水利用的一种良好方式，采用动态规划技术，结合水文预报，气象预报资料的使用，探讨了水库防洪，补源的优化调度方法，调度中采用了能同时反映山东省的超渗产流与蓄潢产流的复合产流模型计算入库洪水，在进行产流和汇流计算和的基础上，建立了防洪，补源优化调度模型，文中介绍了利用太河水库调蓄洪水进行回灌的工作实践，并对其经济效益作了计算分析。     

Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach

Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition.     

Potential Clogging and Dissolution Effects During Artificial Recharge of Groundwater Using Potable Water

To avoid the water quality deterioration that are caused by artificial recharge (AR) of groundwater, potable drinking water has been used as one of the source water for AR to control the side effects caused by the over-exploitation of groundwater. Chemical clogging problems can still be caused by certain chemical components, especially Fe and Al, and a lower concentration of these elements can cause a notable decrease in hydraulic conductivity at the top layer of the infiltration medium. Some components in AR source water can be obstructed by the clogging layer, leading to a change in water quality. The accumulation of total suspended solids (TSS) at the clogging layer can cause physical clogging and worsen the degree of chemical clogging. Although clogging and the related change in water quality were the dominant issues that affect the infiltration rate and health risks during the AR process, the dissolution of the aquifer matrix should also be taken into account. This dissolution contributed to not only the hydraulic conductivity of the infiltration medium but also the potential change in water quality during the aquifer recharge, storage and recovery processes.     

Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.     

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.

     

改性纳米零价铁修复重金属污染地下水研究

纳米零价铁(n ZVI)粒径小、比表面积大、反应活性高,可降解多种污染物,但n ZVI分散性差、抗氧化性弱的特点使其在地下水修复中的应用受到限制。改性n ZVI可以提高n ZVI的分散性及稳定性,研究了现有改性n ZVI的特点:多孔材料负载改性n ZVI成本较低,但可选择的材料相对较少;有机物包覆分散改性n ZVI的改性材料较多,可以促进非水相液体污染物与n ZVI的接触反应,但选择不当容易引起二次污染;双金属改性纳米材料的反应速度较快,不易形成腐蚀产物,但成本较高,且过渡金属可能对环境有害。n ZVI可以处理多种地下水重金属污染物,根据Fe2+和目标重金属离子污染物的标准电极电位的相对大小,降解方式分为表面吸附—配合作用、表面吸附—还原作用和表面还原作用,但是改性n ZVI存在价格高、有环境毒性等问题。     

Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions,Northwest China

Prediction of groundwater depth (GWD) is a critical task in water resources management. In this study, the practicability of predicting GWD for lead times of 1, 2 and 3 months for 3 observation wells in the Ejina Basin using the wavelet-artificial neural network (WA-ANN) and wavelet-support vector regression (WA-SVR) is demonstrated. Discrete wavelet transform was applied to decompose groundwater depth and meteorological inputs into approximations and detail with predictive features embedded in high frequency and low frequency. WA-ANN and WA-SVR relative of ANN and SVR were evaluated with coefficient of correlation (R), Nash-Sutcliffe efficiency (NS), mean absolute error (MAE), and root mean squared error (RMSE). Results showed that WA-ANN and WA-SVR have better performance than ANN and SVR models. WA-SVR yielded better results than WA-ANN model for 1, 2 and 3-month lead times. The study indicates that WA-SVR could be applied for groundwater forecasting under ecological water conveyance conditions.     

Modeling of Groundwater Artificial Recharge by Reactivating an Old Stream Bed

This paper refers to the development and verification of a groundwater artificial recharge model in Xanthi plain, Thrace, Greece. The relative geological and hydrogeological research of the study area is described, as well as the artificial recharge application by reactivating an old stream bed of Kosynthos River, since 1994. The simulation of the aquifer system of the study area was achieved by the use of MODFLOW and involves both the model calibration and prediction of the aquifer system response. The verification of the 9-year-period model prediction for the first 7 years (1995–2001) proved successful, while the respective procedure for the last 2 years, 2002 and 2003, could not result in acceptable estimations.     

Demonstration of Sustainable Development of Groundwater through Aquifer Storage and Recovery (ASR)

Water Resources Management - Sustained pumping of groundwater can lead to declining water levels in wellfields and concerns regarding the sustainability of groundwater resources. Aquifer Storage...     

Recharge Wells Site Selection for Artificial Groundwater Recharge in an Urban Area Using Fuzzy Logic Technique

In arid and semi-arid area, groundwater is the most important water resources. Surface runoff harvesting is the most important process in the artificial recharge of groundwater that should increase groundwater quality and quantity. Urban impervious area provides an appropriate surface to produce adequate amounts of runoff. Groundwater recharge via recharge wells is one of the successful direct sub-surface methods. As many cities around the world face issues of water scarcity due to a fast and unsustainable urbanization, identify the best locations of groundwater recharge wells is an interesting relevant topic, especially in the arid and semi-arid area. Selection of a suitable area for groundwater recharge could increase efficiency of the recharge wells. In this study, the best location of recharge wells was investigated in Urmia city located in the North-west of Iran using fuzzy logic technique. In this study, locations of the drainage channel junctions with adequate potential of surface runoff were determined using SWMM. Appropriate locations for recharge wells were determined based on different layers including distance to runoff harvesting points, distance to the production water wells and depth of groundwater table. Hydraulic condition (hydraulic conductivity and specific recharge) was also used separately. The input layers were prepared using geostatistical interpolation techniques in ArcGIS 9.3 software. Mamdani fuzzy inference system was applied to incorporate the fuzzified input layers. Finally, in each area, pixels with the highest value were proposed as suitable locations for recharge wells. Based on the results, the number of pixels with “High” priorities increased when the hydraulic conductivity was used to site selection. Comparing hydraulic conductivity layer and selected location of the recharge wells shows that the area with low hydraulic conductivity and the area closed to the production water wells has not suitable priority for recharge wells.     

Risk Assessment of Groundwater and its Application. Part II: Using a Groundwater Risk Maps to Determine Control Levels of the Groundwater

With the rapid development of economy, demand of water resources is becoming larger and larger, and over-exploitation of groundwater is common in many areas. Due to over-exploitation of groundwater over many years, a number of potential adverse hydrogeological problems have raised. To reduce such adverse effects, it is necessary to carry out strict groundwater management in over-exploited areas. And to achieve the strictest management of groundwater, it is critical to determine control levels of groundwater including the blue line levels (proper levels) and red line levels (warning levels). According to the establishment of evaluation model of shallow and deep groundwater exploitation and utilization risks, it can be observed that the groundwater level index factor is included in the evaluation index system in different groundwater function zones. Therefore, there is a corresponding relationship between the risk grade and groundwater level of different underground aquifers. The risk grade of different groundwater function zones in Tianjin is divided into five grades, which contributes to the risk management of groundwater, avoiding the arising of a wide range of risk management measure. However, to determine the key groundwater level, the standard of five grades cannot meet the requirements. The risk grades need to be divided more subtly. Hence, in this paper, the risk grade was divided according to the standard of sixteen grades based on that of five grades in the first place. The higher the grade is, the greater the risk. And then the occurrence frequency of risk grade for each aquifer was counted in each administrative district or country. The corresponding water level of the risk grade, whose occurrence frequency was the highest, served as the base level. The water level of groundwater that would be exploited and utilized in the future cannot be below this base level. In consequence, this water level that served as the red line level was the minimum requirement in the planning years, while the corresponding water levels of other risk grades that were inferior to this risk grade can all be seen as red line levels. And the planning period the long-term corresponding groundwater level of the aquifers under mining-banned condition can be used as blue line control levels of the different planning years. Finally, according to the determinate range of red line level change amplitude in each district or country, as well as the ultimate restoration aim of groundwater levels (blue line levels), corresponding measures were taken step by step to achieve the overall rising of groundwater levels. The obtained determinate control levels can provide a scientific basis for dynamic management of groundwater.