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1.
Neural network prediction of nitrate in groundwater of Harran Plain, Turkey   总被引：2，自引：0，他引：2  
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.  相似文献   

2.
In recent years, nitrate contamination of groundwater has become a growing concern for people in rural areas in North China Plain (NCP) where groundwater is used as drinking water. The objective of this study was to simulate agriculture derived groundwater nitrate pollution patterns with artificial neural network (ANN), which has been proved to be an effective tool for prediction in many branches of hydrology when data are not sufficient to understand the physical process of the systems but relative accurate predictions is needed. In our study, a back propagation neural network (BPNN) was developed to simulate spatial distribution of NO3-N concentrations in groundwater with land use information and site-specific hydrogeological properties in Huantai County, a typical agriculture dominated region of NCP. Geographic information system (GIS) tools were used in preparing and processing input–output vectors data for the BPNN. The circular buffer zones centered on the sampling wells were designated so as to consider the nitrate contamination of groundwater due to neighboring field. The result showed that the GIS-based BPNN simulated groundwater NO3-N concentration efficiently and captured the general trend of groundwater nitrate pollution patterns. The optimal result was obtained with a learning rate of 0.02, a 4-7-1 architecture and a buffer zone radius of 400 m. Nitrogen budget combined with GIS-based BPNN can serve as a cost-effective tool for prediction and management of groundwater nitrate pollution in an agriculture dominated regions in North China Plain.  相似文献   

3.
Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA–ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R 2), Nash–Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA–ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA–ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.  相似文献   

4.
Jinci Spring in Shanxi, north China, is a major local water source. It dried up in April 1994 due to groundwater overexploitation. The groundwater system is complex, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are statistical techniques to study parameter nonlinear relationships of groundwater systems. However, ANN models offer little explanatory insight into the mechanisms of prediction models. Sensitivity analysis can overcome this shortcoming. In this study, a back-propagation neural network model was built based on the relationship between groundwater level and its sensitivity factors in Jinci Spring Basin; these sensitivity factors included precipitation, river seepage, mining drainage, groundwater withdrawals and lateral discharge to the associated Quaternary aquifer. All the sensitivity factors were analyzed with Garson’s algorithm based on the connection weights of the neural network model. The concept of “sensitivity range” was proposed to describe the value range of the input variables to which the output variables are most sensitive. The sensitivity ranges were analyzed by a local sensitivity approach. The results showed that coal mining drainage is the most sensitive anthropogenic factor, having a large effect on groundwater level of the Jinci Spring Basin.  相似文献   

5.
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.  相似文献   

6.
Neural network assessment of groundwater contamination of US Mid-continent   总被引：1，自引：0，他引：1  
Artificial neural networks (ANNs) were applied to data taken from 1,302 domestic and rural hydraulic wells in the Mid-continent of the USA including Illinois, Iowa, and other 12 States to predict the contamination of the groundwater with pesticides. Preliminary hydrogeological and geostatistical analyses were carried out to assess groundwater vulnerability and data variability and weight, where data attributes were pre-processed and grouped into three main categories: hydrologic, human interaction, and climatic groups. ANNs are computer parallel-based systems that are characterized by their topologies, transfer functions, and learning algorithms. The backpropagation network (BP-NN) learning algorithm, used here, involves incremental adjustment of a set of parameters to minimize the error between the desired output and the actual output. Sensitivity analysis of the main BP-NN attributes was conducted to improve the BP-NN performance. Results of several trials demonstrated that the BP-NN have predicted the contaminated wells within each minor group in high precision. Sensitivity analysis revealed that BP-NN topologies and transfer functions were the main factors that affected its performance. It is evident that BP-NN is a powerful tool to predict the groundwater contamination over a wide area with limited data availability, which can provide an alternative cheap and effective tool to assess groundwater contamination worldwide.  相似文献   

7.
Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production.Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines.Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.  相似文献   

8.
A logistic regression model for the probability of arsenic exceeding the drinking water guidelines (10 μg/L) in bedrock groundwater was developed for a selected county in Korea, where arsenic occurrence and release reactions have been investigated. Arsenic was enriched naturally by the oxidation of sulfide minerals in metasedimentary rocks and mineralized zones, and due to high mobility in alkaline pH conditions, concentrations were high in groundwater of the county. When considering these reactions of arsenic release and water quality characteristics, several geological and geochemical factors were selected as influencing variables in the model. In the final logistic regression model, geological units of limestone and metasedimentary rocks, the concentrations of nitrate and sulfate, and distances to closed mines and adjacent granite were retained as statistically significant variables. Predicted areas of high probability agreed well with known spatial contamination patterns in the county. The model was also applied to an adjacent county, where the groundwater has not previously been tested for the presence of arsenic, and a probability map for arsenic contamination was then produced. Through the analysis of arsenic concentrations at the wells of high probability, it was determined that the applied model accurately indicated the arsenic contamination of groundwater. The logistic regression approach of this study can be applied to predict arsenic contamination in areas of similar geological and geochemical conditions to the county used in this model.  相似文献   

9.
In many rural communities, groundwater is used to meet the water demand of the community and for the irrigation of cultivating areas. The quality of groundwater can be adversely affected by agricultural activities and finally groundwater quality may become unsuitable for human consumption and irrigation, as in the Harran Plain. Hence, monitoring groundwater quality by cost-effective techniques is necessary, as especially unconfined aquifers are vulnerable to contamination. This study presents an artificial neural network model predicting sodium adsorption ratio (SAR) and sulfate concentration in the unconfined aquifer of the Harran Plain. Samples from 24 observation wells were analyzed monthly for 1?year. Electrical conductivity, pH, groundwater level, temperature, total hardness and chloride were used as input parameters in the predictions. The best back-propagation (BP) algorithm and neuron numbers were determined for the optimization of the model architecture. The Levenberg?CMarquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 20 for both parameters. The model tracked the experimental data very closely both for SAR (R?=?0.96) and sulfate (R?=?0.98). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.  相似文献   

10.
为可靠预测基坑周边地表沉降的发展趋势,提出了一种基于混合蛙跳算法和广义回归神经网络模型的基坑地表最大沉降预测模型（SFLA-GRNN模型）。首先,在沉降机制分析并初选输入变量集的基础上,利用灰色相关度分析对模型输入、输出变量的相关性进行量化,并剔除与输出变量相关性明显偏小的输入变量;其次,利用混合蛙跳算法（SFLA）对广义回归神经网络模型（GRNN）的平滑因子进行优化确定,减少人为因素对模型精度和泛化能力的不良影响;最后,利用筛选得到的输入变量集建立基坑地表最大沉降预测的广义回归神经网络模型。实例应用及对比计算结果表明,基于灰色相关度的输入变量筛选和基于混合蛙跳算法的平滑因子优化均能够有效提高广义回归神经网络模型的精度和泛化能力,以上结论可为类似变形预测提供参考。  相似文献   

11.
The mineral resource estimation requires accurate prediction of the grade at location from limited borehole information. It plays the dominant role in the decision-making process for investment and development of various mining projects and hence become an important and crucial stage. This paper evaluvates the use of two distinct artificial neural network (ANN)-based models, general regression neural network (GRNN) and multilayer perceptron neural network (MLP NN), to improve the grade estimation from Koira iron ore region in Sundargarh district, Odisha. ANN-based models capture the inherent complex structure of mineral deposits and provide a reliable generalization of the iron grade. The ANN-based approach does not require any preliminary geological study and is free from any statistical assumption on the raw data before its application. The GRNN is a one-pass learning algorithm and does not require any iterative procedure for training less complex structure and requires only one learning parameter for optimization. In this investigation, the spatial coordinates and multiple lithological units were taken as input variables and the iron grade was taken as the output variable. The comparative analysis of these models has been carried out and the results obtained were validated with traditional geostatistical method ordinary kriging (OK). The GRNN model outperforms the other methods, i.e. MLP and OK, with respect to generalization and predictability of the grades at an un-sampled location.  相似文献   

12.
One of the most important qualitative aspects of wetland ecosystem management is preserving the natural quality of water in such environments. This would not be achievable unless continuous water quality monitoring is implemented. With the recent advances in remote sensing technology, this technology could assist us to produce accurate models for estimating water quality variables in the ecosystem of wetlands. The present study was carried out to evaluate the capability of remote sensing data to estimate the water quality variables [pH, total suspended solids (TSS), total dissolved solids (TDS), turbidity, nitrate, sulfate, phosphate, chloride and the concentration of chlorophyll a] in Zarivar International Wetland using linear regression (LR) and artificial neural network (ANN) models. For this purpose, spectral reflectance of bands 2, 3, 4 and 5 of the OLI sensor of Landsat 8 was utilized as the input data and the collected chemical and physical data of water samples were selected as the objective data for both ANN and LR models. Based on our results overall, ANN model was the proper model compared with LR model. The spectral reflectance in bands 5 and 4 of OLI sensor revealed the best results to estimate TDS, TSS, turbidity and chlorophyll in comparison with other used bands in ANN model, respectively. We conclude that OLI sensor data are an excellent means for studying physical properties of water quality and comparing its chemical properties.  相似文献   

13.
The aim of this research was to predict groundwater levels in the Neishaboor plain, Iran, using a ??panel-data?? model. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time span. Firstly, the available observation wells in the Neishaboor plain were clustered according to their fluctuation behavior using the ??Ward?? method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation and temperature, as independent variables, were estimated by the inverse-distance method. Finally, the performance of different panel-data regression models such as fixed-effects and random-effects models were investigated. The results showed that the two-way fixed-effects model was superior. The performance indicators for this model (R 2?=?0.97, RMSE?=?0.05?m and ME?=?0.81?m) reveal the effectiveness of the method. In addition, the results were compared with the results of an artificial-neural-network (ANN) model, which demonstrated the superiority of the panel-data model over the ANN model.  相似文献   

14.
This paper is the result of a study which was carried out in order to verify if the traditional methods to evaluate the intrinsic vulnerability or vulnerability related parameters, are able to clarify the problem of nitrate pollution in groundwater. In particular, the aim was to evaluate limitations and problems connected to aquifer vulnerability methods applied to nitrate contamination prevision in groundwater. The investigation was carried out by comparing NO3 − concentrations, measured in March and November 2004 in the shallow aquifer, and the vulnerability classes, obtained by using GOD and TOT methods. Moreover, it deals with a comparison between NO3 − concentrations and single parameters (depth to water table, land use and nitrogen input). The study area is the plain sector of Piemonte (Northern Italy), where an unconfined aquifer nitrate contamination exists. In this area the anthropogenic presence is remarkable and the input of N-fertilizers and zootechnical effluents to the soil cause a growing amount of nitrates in groundwater. This approach, used in a large area (about 10,000 km2) and in several monitoring wells (about 500), allowed to compare the efficiency of different vulnerability methods and to verify the importance of every parameter on the nitrate concentrations in the aquifer. Furthermore it allowed to obtain interesting correlations in different hydrogeological situations. Correlations between depth to water table, land use and nitrogen input to the soil with nitrate concentrations in groundwater show unclear situations: in fact these comparisons describe the phenomenon trend and highlight the maximum nitrate concentrations for each circumstance but often show wide ranges of possible nitrate concentrations. The same situation could be observed by comparing vulnerability indexes and nitrate concentrations in groundwater. These results suggest that neither single parameters nor vulnerability methods (GOD and TOT) are able to describe individually the complex phenomena affecting nitrate concentrations in soil, subsoil and groundwater. In particular, the traditional methods for vulnerability analysis do not analyze physical processes in aquifers, such as denitrification and nitrate dilution. According to a recent study in the shallow unconfined aquifer of the Piemonte plain, dilution can be considered as the main cause for nitrate attenuation in groundwater.  相似文献   

15.
The calculation of groundwater reserves in shaly sand aquifers requires a reliable estimation of effective porosity and permeability; the amount of shaliness as a related quantity can be determined from well log analysis. The conventionally used linear model, connecting the natural gamma-ray index to shale content, often gives only a rough estimate of shale volume. A non-linear model is suggested, which is derived from the factor analysis of well-logging data. An earlier study of hydrocarbon wells revealed an empirical relationship between the factor scores and shale volume, independent of the well site. Borehole logs from three groundwater wells drilled in the northeastern Great Hungarian Plain are analyzed to derive depth logs of factor variables, which are then correlated with shale volumes given from the method of Larionov. Shale volume logs derived by the statistical procedure are in close agreement with those derived from Larionov’s formula, which confirms the validity of the non-linear approximation. The statistical results are in good accordance with laboratory measurements made on core samples. Whereas conventional methods normally use a single well log as input, factor analysis processes all available logs to provide groundwater exploration with reliable estimations of shale volume.  相似文献   

16.
Sustaining the human ecological benefits of surface water requires carefully planned strategies for reducing the cumulative risks posed by diverse human activities. The municipality of Aksaray city plays a key role in developing solutions to surface water management and protection in the central Anatolian part of Turkey. The responsibility to provide drinking water and sewage works, regulate the use of private land and protect public health provides the mandate and authority to take action. The present approach discusses the main sources of contamination and the result of direct wastewater discharges into the Melendiz and Karasu rivers, which recharge the Mamasın dam sites by the use of artificial neural network (ANN) modeling techniques. The present study illustrates the ability to predict and/or approve the output values of previously measured water quality parameters of the recharge and discharge areas at the Mamasin dam site by means of ANN techniques. Using the ANN model is appreciated in such environmental research. Here, the ANN is used for estimating if the field parameters are agreeable to the results of this model or not. The present study simulates a situation in the past by means of ANN. But in case any field measurements of some relative parameters at the outlet point “discharge area” have been missed, it could be possible to predict the approximate output values from the detailed periodical water quality parameters. Because of the high variance and the inherent non-linear relationship of the water quality parameters in time series, it is difficult to produce a reliable model with conventional modeling approaches. In this paper, the ANN modeling technique is used to establish a model for evaluating the change in electrical conductivity (EC) and dissolved oxygen (DO) values in recharge (input) and discharge (output) areas of the dam water under pollution risks. A general ANN modeling scheme is also recommended for the water parameters. The modeling process includes four main stages: (1) source data analysis, (2) system priming, (3) system fine-tuning and (4) model evaluation. Results of the ANN modeling scheme indicate that the output values are agreeable to the water quality parameters, which were measured at the field in the static water mass of the Mamasın dam lake. Water contamination at the dam site is caused by the continuous increase of nutrient contents and decrease of the O2 level in water causing an anaerobic condition. It may stimulate algae growth flow in such water bodies, consequently reducing water quality.  相似文献   

17.
The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56° N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n + 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models.  相似文献   

18.
基于ANN模型重塑岩溶地下河系统流量数据可行性研究   总被引：1，自引：1，他引：0  
程庭  陈植华  时坚  卢小慧 《中国岩溶》2006,25(2):121-125
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。   相似文献   

19.
杨平  王新民  路来君 《地学前缘》2016,23(3):151-155
文中首先运用了一种改进的数量化理论I模型作为预处理工具,对影响地下水水质的20个因子进行定性数据转换、数据降维,随后将8个重要特征因子作为RBF（径向基函数）神经网络模型的输入,进一步对监测井的采样数据进行学习、训练,揭示地下水污染质迁移转化规律。尝试用经过改进的数量化理论与RBF神经网络方法二者结合,对沈阳李官水源地研究区监测井地下水水质变化进行模拟与预测,其仿真结果覆盖了现有的绝大部分实测数据,适用范围广泛,具有一定的推广价值。  相似文献   

20.
A MATLAB based backpropagation neural network (BPNN) model has been developed. Two major geo-engineering applications, namely, earth slope movement and ground movement around tunnels, are identified. Data obtained from case studies are used to train and test the developed model and the ground movement is predicted with the help of input variables that have direct physical significance. A new approach is adopted by introducing an infiltration coefficient in the network architecture apart from antecedent rainfall, slope profile, groundwater level and strength parameters to predict the slope movement. The input variables for settlement around underground excavations are taken from literature. The neural network models demonstrate a promising result predicting fairly successfully the ground behavior in both cases. If input variables influencing output goals are clearly identified and if a decent number of quality data are available, backpropagation neural network can be successfully applied as mapping and prediction tools in geotechnical investigations.  相似文献   

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