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.     

Groundwater Remediation Design Underpinned By Coupling Evolution Algorithm With Deep Belief Network Surrogate

Water Resources Management - Groundwater remediation design is crucial to contemporary water resources management, which is prone to massive computational costs due to the complexity and...     

An Adaptive Dynamic Kriging Surrogate Model for Application to the Optimal Remediation of Contaminated Groundwater

Water Resources Management - When using the simulation–optimization model to optimize groundwater extraction-treatment schemes, constructing a surrogate model for the numerical simulation...     

Multiple Hydropower Reservoirs Operation by Hyperbolic Grey Wolf Optimizer Based on Elitism Selection and Adaptive Mutation

Multiple hydropower reservoirs operation is an effective measure to rationally allocate the limited water resources under uncertainty. With the rapid expansion of water resources system, it becomes much more difficult for traditional methods to quickly yield the reasonable operational policy. Grey wolf optimizer, inspired by the wolves’ hunting behaviors, is a famous metaheuristic method to resolve engineering optimization problems, but still suffers from the local convergence and search stagnation defects. To alleviate this problem, this study proposes a hybrid grey wolf optimizer (HGWO) where the hyperbolic accelerating strategy is introduced to improve the local search ability; the adaptive mutation strategy is used to diversify the swarm; the elitism selection strategy is used to enhance the convergence speed. The experimental results show that the HGWO method can produce better solutions than its original version in several test functions. Then, the HGWO method is applied to resolve the optimal operation of a real-world hydropower system with the goal of maximizing the total generation benefit. The simulations indicate that the HGWO method produces satisfying scheduling schemes than several control methods in terms of all the statistical indicators. Hence, with the merits of superior search ability, rapid convergence rate and gradient information avoidance, HGWO proves to be a promising alternative optimization tool for the complex multireservoir system operation problem.

     

水利水电工程人工神经网络综合优选模型

在分析水利水电工程常用综合优选模型应用中存在问题的基础上，将综合优选问题的特点和人工神经网络原理有机结合，建立了方案综合优选的人工神经网络拓扑结构；设计了相应的网络学习算法，提出了生成训练样本和方案优选方法，实例分析结果证明模型和方法是实用、有效的。     

Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout

The simulation-optimization approach is often used to solve water resource management problem although repeated use of the simulation model enhances the computational load. In this study, Artificial Neural Network (ANN) and Bagged Decision Trees (BDT) models were developed as an approximator for Analytic Element Method (AEM) based groundwater flow model. Developed ANN and BDT models were coupled with Particle Swarm Optimization (PSO) model to solve the well-field management problem. The groundwater flow model was developed for the study area and used to generate the dataset for the training and testing of the ANN & BDT models. These coupled ANN-PSO & BDT-PSO models were employed to find the optimal design and cost of the new well-field system by optimizing discharge & co-ordinate of wells along with the cost effective layout of piping network. The Minimum Spanning Tree (MST) based model was used to find out the optimal piping network layout and checking the hydraulic constraints in the piping network. The results show that the ANN & BDT models are good approximators of AEM model and they can reduce the computational burden significantly although ANN model performs better than BDT model. The results show that the coupling of piping network model with simulation-optimization model is very significant for finding the cost effective and realistic design of the new well-field system.     

基于神经网络的城市日用水量预测

分析了影响城镇日用水量的非线性因素,利用人工神经网络,选择影响城市日用水量的主要因素。建立城镇日用水量预测模型,并将该模型的预测效果与传统的日用水量模型预测效果进行比较,结果显示该模型的预测精度更高、所需时间更短、更适用于影响因素较多的城市日用水量的预测。     

基于中心逼近式灰色神经网络模型的水质预测

对时间序列建立中心逼近式GM(1,1)模型,通过优选模型的m值弱化序列变幅,利用BP神经网络对该模型残差值进行拟合修正,以此构建一个基于中心逼近式GM(1,1)模型的灰色神经网络预测模型.应用实例的计算结果表明,该模型可提高水质预测精度.     

Coupled Simulation-Optimization Model for the Management of Groundwater Resources by Considering Uncertainty and Conflict Resolution

Water Resources Management - Determining the optimized policies in the exploitation of groundwater water resources is a complicated issue, especially when there are several different managers with...     

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.     

基于人工神经网络和正规化周期回归的耦合模型及其应用

利用方差分析周期外推方法,提出了改进的正规化周期回归模型,将该模型应用到新丰江流域的径流序列,进行模拟和预测,计算结果表明：长期趋势项和周期项拟合后基本上能够反映出实测径流序列的特征和变化,但预报的精度还不够高,为此,提出了基于人工神经网络和正规化周期回归的耦合模型,对周期回归模型分离出来的随机项时间序列进行BP神经网络的分析、计算预测,并且把预测的结果拟合到周期回归模型的预测结果中,从而达到了从整体上提高预测精度的目的。     

基于人工神经网络模型的地下水水位动态变化模拟

地下水水位的预测在流域地表水和地下水资源的综合规划管理中起着非常重要的作用。在该研究中,人工神经网络模型被应用于希尼尔水库周边地下水水位的预测中。采用研究区6口地下水观测井资料,用人工神经网络模型进行模拟预测1周后的地下水水位。模型输入因子包括此前1周蒸发量、水库水位、排渠水位、抽水量和观测井地下水位,因此模型有15个输入节点和6个输出节点。将3种不同的神经网络训练算法,即自适应学习速率动量梯度下降反向传播算法(GDX)、LM算法和贝叶斯正则化算法(BR)用于地下水水位预测,并对模拟结果进行了评估。结果表明:3种神经网络训练算法在研究区地下水水位预测中表现均较好。然而,BR算法的性能总体略优于GDX和LM算法。将BR算法训练的人工神经网络模型用于预测研究区未来2、3和4周的地下水水位,虽然地下水位预测的准确性随着时间的增加有所降低,但模拟效果仍然较好。     

基于改进人工蜂群算法的地下水埋深预测研究

为了改进人工蜂群算法的RBF神经网络模型在地下水埋深预测中的应用,在基本人工蜂群算法中引入高斯变异算子,并优化初始蜜源位置,提出了基于改进人工蜂群算法的RBF神经网络模型,并利用安阳市某观测站的降水量、蒸发量、河道流量、灌溉渗漏量和人工开采量5个相关影响因子的数据,对该方法进行了应用。为了验证模型的优劣性,与单一的BP神经网络模型、RBF神经网络模型、基于蚁群算法的RBF神经网络模型和基于基本人工蜂群算法的RBF神经网络模型的预测结果进行了比较,结果表明:基于改进人工蜂群算法的RBF神经网络模型收敛速度更快、预测结果误差最小。     

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.     

基于灰色马尔可夫链的兰村泉域地下水位预测

将兰村泉域S1长观孔2001—2010年的年平均水位埋深作为特征因素序列,降水量和人工开采量作为相关因素序列,采用灰色系统理论将GM(1,N)模型应用于兰村泉域岩溶地下水位埋深预测,并应用马尔可夫模型对输出结果进行残差修正。结果表明:经过修正后的GM(1,3)模型的拟合精度达到97.41%,比没有经过残差修正的GM(1,3)模型高出9.62%,修正后的预测值更加贴近原始值,准确性提高。采用马尔可夫残差修正模型对2011—2013年兰村泉域水位埋深值进行预测,结果表明:2011年、2012年、2013年的地下水位预测值分别为33.24、32.01、31.12 m,地下水位缓慢回升。     

Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

Water Resources Management - Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural...     

偏最小二乘法与神经网络耦合的大坝监测模型

针对人工神经网络在大坝变形监测模型应用中所出现的收敛慢和稳定性差等问题,提出了偏最小二乘法与人工神经网络耦合的大坝变形监测模型,提高了神经网络的学习速率和稳定性.首先运用偏最小二乘法对多维自变量进行主成分提取和降维处理,解决了变量之间的多重相关问题,而后把降维的数据输入神经网络进行训练.对比实例应用结果表明,偏最小二乘神经网络耦合模型的拟合速度和精度都高于传统的神经网络.     

基于改进人工神经网络的梯级水电站群调度规则研究

针对水电站群的运行依靠经验、缺少科学依据的问题,根据人工神经网络的非线性决策特点,提出了水电站群最优调度规则的改进人工神经网络模型,并结合实际算例,进行其最优调度规则的模拟计算。结果表明,模型合理、可行且实用。     

基于人工鱼群算法的冰情预报神经网络模型

在分析凌汛成因的基础上选取合适的预报因子,针对BP神经网络收敛速度慢、易陷入局部极小值的缺点,利用改进的人工鱼群算法训练BP神经网络,以黄河宁蒙河段封开河日期数据进行建模,给出了人工鱼群算法训练神经网络的基本原理和步骤,并对人工鱼群算法神经网络模型、遗传算法神经网络模型、粒子群神经网络模型的预测结果进行了对比分析。结果表明:人工鱼群算法神经网络模型对黄河内蒙古段凌汛期的封开河日期预测比较准确,预测结果优于遗传算法神经网络模型和粒子群神经网络模型。     

C++ Builder编程实现BP网络城市可持续发展调控预测模型

在总结非线性时间序列预测模型的基础上,将城市可持续发展调控预测和人工神经网络BP算法相结合,通过查询相关城市可持续发展调控预测资料及网上搜索,确定作为模型预报因子的指数,再筛选影响城市可持续发展调控预测的相关指标,对原始数据进行处理,选取影响城市可持续发展调控预测的主要因素,提出了基于神经网络的城市可持续发展调控预测模型,并借助于C＋＋Builder编程来实现.