基于黄金分割理论的改进BP网络地下水水质评价

利用VB语言编写附加动量的改进BP人工神经网络模型程序,并将其加载到Excel中,以湛江市区地下水为例研究水质状况。该模型采用黄金分割理论和试算相结合的方法对网络模型的隐含层节点数进行了优选,研究结果与其他方法相比显示：改进BP人工神经网络模型在地下水水质评价中能够很好地解决评价因子与水质等级间复杂的非线形关系,评价结果的精度有较大地提高。     

德州市农村人饮工程地下水源水质评价

根据817眼地下水监测井的水质普查监测资料,对德州市全市范围内的地下水,采用单因子评价指数法和内梅罗综合指数法进行了水质评价,为德州市农村人饮工程地下水源的科学规划提供了依据。     

咸阳市地下水重点污染物分布与污染因素分析

根据分布在咸阳秦都、渭城、兴平、武功等十一个县(区、市)的45个地下水监测井所得水质总硬度、氟化物、氯化物、硝酸盐、六价铬、硫酸盐及溶解性总固体七个重要污染指标数据,得出每一类污染物的具体空间分布,从水文地质、工农业生产、人类生活几个方面对地下水污染严重地区进行具体原因分析,结果得到人类活动所排放的污染物尤其是工农业生产污水对地下水水质影响最大;采用模糊综合分析法计算每个监测井水质对于五级水分类标准的不同隶属度,得出研究区内地下水质呈现南部普遍差于北部的结果;进一步总结硝酸盐、溶解性总固体与氯化物、总硬度几个污染指标之间的正相关关系,及水质pH呈弱碱性有利于氟化物浓度升高等关系,为之后的地下水治理提供参考。     

地下水监测工程承压-自流监测井密封技术

针对地下水监测工程施工中面临的承压-自流监测井密封、监测、取样、洗井等紧迫难题,研发了承压-自流监测井密封技术。该技术是在监测井管口安装一个具有多个功能组件的密封不锈钢盖板来实现自流监测井长期监测的各项需求。本文介绍了承压-自流监测井密封技术原理、系统组成和安装流程及方法。通过山西忻州市地下水监测井现场示范及其他地区50余口井的工程应用,证实该技术可有效解决自流监测井井口密封、水样采集、洗井清淤和维护探头等一系列问题,具有密封效果好、不污染水质、操作便捷和成本低等优点,可实现不同特征自流监测井地下水数据长期自动采集与传输,并可为类似井的封孔工作提供解决思路。     

南京市岩溶地下水质量的模糊评价

采用模糊数学方法,选取了6个指标作为评价因子,建立了模糊关系矩阵和评价模型,对南京市的岩溶地下水水质进行了评价,结果反映了南京市的岩溶地下水水质现状,为南京市岩溶地下水资源的规划和管理提供了科学依据.     

上海某生活垃圾填埋场对浅层地下水水质的影响

垃圾填埋是目前处理城市生活垃圾普遍使用的方式,由此产生的垃圾渗滤液成为主要的地下水污染源.本文以上海市某生活垃圾填埋场作为研究对象,研究垃圾填埋对地下水的影响.通过监测该场地垃圾填埋前后2年内场区及周边地下水水质的变化情况,以垃圾填埋前调查区的地下水样品分析结果为本底值,采用本底法对地下水水质进行评价来判定地下水是否受到垃圾渗滤液的影响.评价结果显示,对于本研究的水质动态监测阶段,调查区内的浅层地下水水质暂未受到垃圾渗滤液的影响,个别监测井水质发生较大变化是由于填埋场施工建设过程中,破坏了监测井井盖及挖穿了井边含水层顶层.随着整个垃圾填埋场运行时间的延长,防渗漏措施的有效性以及垃圾渗滤液对周边地下水的影响还需要进一步研究.     

郑州市地下水监测站点监测井施工浅析

地下水过量开采以及水质污染等问题日益突出,严重制约经济社会的发展,开展地下水监测站点建设显得尤为重要。依据《地下水监测井建设规范》,以郑州市2017年度27处地下水监测站点施工为例,对地下水监测井钻探施工、水样采集、辅助设施建设等监测井施工环节进行了分析,以期为同类工程施工提供参考。     

RBF神经网络在深基坑监测预测中的运用

该文简要介绍了：RBF神经网络相对于BP神经网络的优点，分析了RBF神经网络的模型和结构。在此基础上通过Matlab编程语言建立了一预测深基坑工程监测项目的重要内容——墙体位移的RBF神经网络模型，经过工程实例验证了该模型的正确性，说明RBF神经网络在对深基坑工程监测项目的预测是可行和有效的。     

基于MATLAB改进的BP神经网络在地下水水质评价的应用

运用改进的BP神经网络(SCG算法)理论和方法,建立水质评价的BP神经网路模型,以什邡市地下水为例进行评价,并与综合指数法相比较。结果表明,该算法收敛速度快,评价结果较能准确的符合水体水质情况。     

基于遗传BP神经网络的地下水位预测模型

地下水水位动态预测对农田土壤盐渍化防治、地下水地表水资源的合理调度具有十分重要的意义。以新疆和静县某地下水观测井为研究对象,选择月均蒸发量、气温和灌溉量3个因素作为BP神经网络模型的输入量,利用遗传算法优化神经网络的权值与阈值,建立地下水水位的遗传BP神经网络预测模型。结果表明:遗传BP神经网络模型能较好表达地下水位与主控因素之间的非线性关系,预测结果与实测值之间的平均绝对百分比误差为0.040 3,测试样本的网络输出值与网络目标值的相关系数达0.967 3,模型预测效果较佳。研究结果为区域地下水的开发利用与保护提供参考依据。     

SOM-RBF神经网络模型在地下水位预测中的应用

利用自组织映射(SOM)聚类模型优化径向基函数神经网络(RBFN)隐层节点的方法,减小了RBFN由于自身结构问题在地下水水位预测中产生的误差。采用SOM对已有样本进行聚类,利用聚类后的二维分布图确定隐层节点的数目,并根据聚类结果计算径向基函数的宽度,确定径向基函数的中心,由此建立SOM-RBFN模型。以吉林市丰满区二道乡为例,采用2000—2009年观测的地下水位动态资料,利用SOM-RBFN模型对地下水位进行预测,验证其准确性,并分别以5、7、10a的地下水位动态数据为研究样本建立模型,考查样本数量对预测结果的影响。研究结果表明:SOM-RBFN模型预测地下水水位过程中,均方根误差(RMSE)的均值为0.43,有效系数(CE)的均值为0.52,均达到较高标准,因此SOM-RBFN模型可以作为有效而准确的地下水水位预测方法;同时RBF7的RMSE和CE均值分别为0.38和0.68,结果优于RBF5和RBF10,这就意味着在模型计算中样本数量不会直接影响预测结果的精度。     

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.     

人工神经网络(ANN)模型在地下水资源预测中的应用研究

分析了地下水系统影响因素的复杂性，提出对于研究程度不能满足分布参数模型计算要求的研究区域，更适于从系统的观点出发、建立适宜的集中参数模型，从整体上分析研究，以解决相关问题。结合沈阳市地下水资源评价与管理实例，尝试应用人工神经网络(ANN)技术在水资源系统模型研究中的新模式。构建了基于BP算法的ANN降水量和蒸发量的预测、地下水水位动态模拟、预测及开采量优化方面的应用模型，结果表明模型精度满足要求。     

Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas

As a neural network provides a non-linear function mapping of a set of input variables into the corresponding network output, without the requirement of having to specify the actual mathematical form of the relation between the input and output variables, it has the versatility for modeling a wide range of complex non-linear phenomena. In this study, groundwater contamination by nitrate, the ANNs are applied as a new type of model to estimate the nitrate contamination of the Gaza Strip aquifer. A set of six explanatory variables for 139 sampled wells was used and that have a significant influence were identified by using ANN model. The Multilayer Perceptrons (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Linear Networks were used. The best network found to simulate Nitrate was MLP with six input nodes and four hidden nodes. The input variables are: nitrogen load, housing density in 500-m radius area surrounding wells, well depth, screen length, well discharge, and infiltration rate. The best network found had good performance (regression ratio 0.2158, correlation 0.9773, and error 8.4322). Bivariate statistical test also were used and resulting in considerable unexplained variation in nitrate concentration. Based on ANN model, groundwater contamination by nitrate depends not on any single factor but on the combination of them.     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

采场顶板稳定性动态预测及控制研究

运用人工神经网络技术,综合岩石介质条件、赋存环境条件以及工程因素3大方面的5个指标,即岩石单轴抗压强度、岩石质量指标、煤体强度、地下水状况、工作面月推进速度,建立了采场顶板稳定性动态预测模型。并以工作面月推进速度40m、60m、80m、100m分别预测了新集井田顶板稳定性分区。根据5个指标因素分析结果,对顶板稳定性影响程度由大到小排序为岩石质量指标、地下水状况、岩石单轴抗压强度、煤体强度、工作面月推进速度。     

基于ANN模型重塑岩溶地下河系统流量数据可行性研究

西南岩溶地区地下河系统的多层次和多级性特征,决定了其输入因子与响应因子之间为非线性关系,传统的统计方法在揭示此类关系时效果欠佳,而人工神经网络模型( Artificial Neural Ne two rk—— ANN)正好弥补了此项不足,其在原理和构模上均表现出与岩溶地下河系统十分相似的特点。通过对广西地苏地下河系统水量数据的重塑发现, ANN模型重塑的效果明显优于传统的回归分析法,证明了运用ANN模型重塑岩溶地下河系统流量数据是完全可行的。      

Forecasting of groundwater level in hard rock region using artificial neural network

In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development of groundwater resources.     

Environmental hazard analysis of a gypsum karst depression area with geophysical methods: a case study in Sivas (Turkey)

Because the mixture of seawater and freshwater in the Gyeongin-Ara Waterway in South Korea can lead to the intrusion of saline water into surrounding aquifers, systematic management through the establishment of a groundwater protection area is required. The analytic hierarchy process (AHP) model is used to delineate this protection area based on two primary factors and five secondary factors related to saline water intrusion. The study area is divided into 987 gridded cells with a unit size of 100 × 100 m, and the final evaluation score for each cell is calculated using the AHP model. Consequently, several artificial neural network models based on a multilayer perceptron are developed using the AHP’s secondary criteria and the evaluation score. Comparing the evaluation scores of ANN and AHP, more than 180 samples are required in the ANN model to insure high R2 between the original and estimated values. The ANN model is more consistent than the AHP model when determining groundwater protection area, because it can be re-constructed due to the changes in some secondary criteria and also changed due to a standardization process. The final evaluation score by the ANN model based on 300 samples, with the highest R2, is calculated and the regions with a score higher than 2.0 are selected as the groundwater protection area, accounting for 15% of the total cells. This area is similar to the range within approximately 200 m of the GA Waterway and also includes some changing sites in hydrogeochemistry and electric conductivity, which is produced by saline water intrusion. If the land-use type, groundwater levels, and some other criteria change at any cell, the ANN model can be re-executed to verify whether the cell belongs to a groundwater protection area. Considering that salinity of groundwater near the waterway can be affected by various factors including well depth, pumping conditions, and groundwater levels, the ANN model, which is a non-linear model, can be more effective for prediction than the AHP model.     

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network

Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.