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基于层次分析法的表层带岩溶水资源评价方法探讨——以大小井流域为例 总被引：2，自引：0，他引：2
表层岩溶水调节系数是衡量表层岩溶子系统调节地下水能力的一个物理量,其数值与系统内水文地质条件、气象因素及生态环境质量等相关。采用层次分析法量化处理影响表层岩溶系统调节能力的诸因子,建立了表层带岩溶水资源评价模型。评价方法应用在大小井地下河流域的表层岩溶水资源量的评价上取得了较好的效果,其方法对岩溶石山区表层岩溶水资源评价和开发利用规划具有一定的参考价值。 相似文献
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岩溶地下河的存在严重影响了土壤和水评估工具（SWAT）在岩溶地区的普遍适用性。针对岩溶地下河在SWAT中的概化问题,提出基于数字高程模型（DEM）预处理结合SWAT流域自动识别功能的方法,将岩溶地下河暴露于地表,把岩溶区复杂的地表－地下二元结构简化为地表一元结构,并以贵州毕节地下河发育的倒天河流域为例进行应用。结果表明:① 对比未经概化地下河建立的模型,概化地下河建立的模型识别流域面积增大30.73%,子流域个数增加29.27%,水文响应单元（HRU）个数增加43.82%;② 参数取最大物理意义范围时,未经概化地下河建立的模型p因子=0.64,不满足建模条件,物理模型本身存在问题;③ 概化地下河建立的模型月步长模拟结果:校准期R2=0.96,NS=0.96,验证期R2=0.94,NS=0.93,月尺度模拟效果非常好。岩溶地下河概化方法使SWAT在流域划分方面更加合理,搭建的模型模拟更加合理。该研究拓展了SWAT模型在岩溶区的应用。 相似文献
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岩溶地区地下河系统水资源定量评价的问题与出路 总被引：6，自引：2，他引：4
岩溶地区含水介质的多重性和高度复杂性,给地下河系统的水资源量评价带来许多难题。在分析地下河系统水资源形成、分布和运移特征基础上,认为目前用于地下水评价的主要方法都不太适用来解决地下河系统水资源量的评价和预测问题。作者在比较地下河与地表水多方面的相似性后,建议引入现代水文学的理论方法和模型来解决岩溶地区地下河系统水资源评价问题,并分析了可能需要解决的若干关键问题。针对岩溶水资源的特征,作者认为,改变传统定量评价的思维、引进现代水文学理论方法和充分利用3S技术,是解决地下河系统水资源定量评价的主要出路。 相似文献
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典型岩溶水系统碳汇通量估算 总被引：6，自引：1，他引：5
现代岩溶学研究成果表明,碳酸盐岩在全球碳循环中响应极其迅速,水循环过程中的碳汇效应显著。本研究选取广西桂林寨底地下河系统、广西环江大安地下河系统、重庆北碚青木关地下河系统三个典型岩溶地下水系统,利用各系统地下河的流量和HCO3-浓度的监测资料,采用简单化学平衡模式法估算碳汇通量(CO2)。结果显示,寨底地下河系统的单位面积年碳汇通量为68.82 t/(km2.a),大安地下河系统的单位面积年碳汇通量为81.18 t/(km2.a),青木关地下河系统的单位面积年碳汇通量为100.07 t/(km2.a)。分析认为同一个岩溶水系统的结构特征和环境条件基本上是稳定的;地下河的流量和HCO3-浓度是影响岩溶碳汇强度的关键因素,尤其是地下河流量的变化对碳汇强度的影响显著;不同岩溶水系统的碳汇通量不但受水化学条件和地下水动力条件的控制,同时受土地利用变化的影响。该研究对于改进碳循环模型和评价岩溶地质碳汇有重要意义。 相似文献
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岩溶地下河管道空间分布的识别对岩溶区的各类地球科学工作意义重大,文章阐述了采用时延三维电阻率反演技术,开展对地下河管道空间分布识别的研究,在室内灰岩介质下的物理模拟实验结果表明:对雨季管道充水和枯季管道干涸时采集的电阻率数据进行时延反演后,地下河管道的模拟三维空间分布被很好地突显出来,时延反演效果大大地优于对单次采集数据的反演效果,管道充填水时的反演效果次之,管道充填空气时的反演结果很难有效识别地下河管道的空间分布情况。物理模型试验成果可指导野外实践中对岩溶地下河管道的探测研究。 相似文献
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西南岩溶地下河流量重复统计问题及对策探讨 总被引：2，自引：2，他引：0
西南岩溶区的地下河数量及总流量是有关职能部门和专业技术人员关心的两个重要数据。20世纪80年代对西南6省进行统计,结果为2 836条,流量1 482 m3/s;20世纪90年代对西南8省进行统计,并经过近年的数据库校核为2 523条,总流量1 321.7 m3/s;其中流量大于2 000 L/s有120条,合计流量699.7 m3/s,占总流量的52.94%;流量在50~500 L/s地下河最多,有1 311条,占总数的51.55%,其次为流量小于50 L/s的有723条,占总数的28.43%。文章针对上述统计方法存在岩溶地下河流量重复统计、与岩溶大泉流量交叉重复统计两种问题,以大小井和寨底地下河系统为例,给出了具体重复统计量。基于1∶20万水文地质普查资料,大小井和寨底地下河重复统计量分别占总出口流量的57.1%、7.1%;而基于最新调查资料,重复统计量则分别可增加到134.3%, 86.1%。因此,随着1∶5万水文地质调查面积越多,所发现的地下河出口越多,按传统的统计方法其重复统计量则越大。引起上述重复统计问题主要原因是把地下河系统和子系统混合在一起。文章最后讨论了地下河系统的空间属性,提出避免重复统计措施,对西南岩溶区地下河流量正确统计以及具有相同特征的岩溶大泉流量统计有一定指导意义。 相似文献
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岩溶湿地是西南岩溶生态系统的重要调节器,对该地区的可持续发展有重要意义,而水循环作为维系岩溶湿地健康运转的核心因素,已经成为当前的研究热点。2020年4月15日—5月30日野外监测了桂林会仙湿地狮子岩地下河系统参数,采用水文动态分析与水均衡等方法,开展了地下河系统水循环研究。结果表明：（1）地表径流、土壤水、表层岩溶带... 相似文献
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广东黎水地下河开发利用研究 总被引：2，自引：0，他引：2
杨群兴 《水文地质工程地质》2006,33(3):45-48,52
黎水地下河开发工程方案充分利用了地下河流域的区域地质条件、地下河系统特征及水文地质条件等,有效地提高了地下河流域水资源的利用率,且产生了明显的社会、经济和生态环境效益.其成功经验具因地制宜、开发形式多样、一次性投资、见效快及效益高等特点,可为同类或相近类型岩溶流域更好地利用岩溶水资源、弥补地下河时空分布的差异性、解决岩溶石山地区干旱缺水问题提供经验. 相似文献
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Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model 总被引：3，自引：0，他引：3
An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor??s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09?%. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration. 相似文献
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人工神经网络模型在地下水水质评价分类中的应用 总被引：20，自引：0，他引：20
人工神经网络(ArtificialNeuralNetwork以下简称ANN)是一种行之有效的数据处理和分析方法,它的应用领域不断扩大并逐渐完善,本文在传统ANN方法基础上进行了进一步的探讨,立足于BP算法,通过调整ANN输出结构,提高其鲁棒性能,从而使其更具有适应性。将改进后的ANN应用于地下水水质评价分类,并和模糊综合评判评价结果进行了比较,分类结果令人满意。 相似文献
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Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling 总被引：3，自引：1，他引：2
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
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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
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The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN. 相似文献
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Pile foundations are usually used when the conditions of the upper soil layers are weak and unable to support the super-structural loads. Piles carry these super-structural loads deep into the ground. Therefore, the safety and stability of pile-supported structures depends largely on the behavior of the piles. In addition, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile behavior based on the results of cone penetration test (CPT) data. Approximately 500 data sets, obtained from the published literature, are used to develop the ANN model. The paper compares the predictions obtained by the ANN with those given by a number of traditional methods and it is observed that the ANN model significantly outperforms the traditional methods. An important advantage of the ANN model is that the complete load-settlement relationship is captured. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design. 相似文献
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Predictive modeling of hydrological time series is essential for groundwater resource development and management. Here, we examined the comparative merits and demerits of three modern soft computing techniques, namely, artificial neural networks (ANN) optimized by scaled conjugate gradient (SCG) (ANN.SCG), Bayesian neural networks (BNN) optimized by SCG (BNN.SCG) with evidence approximation and adaptive neuro-fuzzy inference system (ANFIS) in the predictive modeling of groundwater level fluctuations. As a first step of our analysis, a sensitivity analysis was carried out using automatic relevance determination scheme to examine the relative influence of each of the hydro-meteorological attributes on groundwater level fluctuations. Secondly, the result of stability analysis was studied by perturbing the underlying data sets with different levels of correlated red noise. Finally, guided by the ensuing theoretical experiments, the above techniques were applied to model the groundwater level fluctuation time series of six wells from a hard rock area of Dindigul in Southern India. We used four standard quantitative statistical measures to compare the robustness of the different models. These measures are (1) root mean square error, (2) reduction of error, (3) index of agreement (IA), and (4) Pearson’s correlation coefficient (R). Based on the above analyses, it is found that the ANFIS model performed better in modeling noise-free data than the BNN.SCG and ANN.SCG models. However, modeling of hydrological time series correlated with significant amount of red noise, the BNN.SCG models performed better than both the ANFIS and ANN.SCG models. Hence, appropriate care should be taken for selecting suitable methodology for modeling the complex and noisy hydrological time series. These results may be used to constrain the model of groundwater level fluctuations, which would in turn, facilitate the development and implementation of more effective sustainable groundwater management and planning strategies in semi-arid hard rock area of Dindigul, Southern India and alike. 相似文献
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以晋祠泉域为例,分析该泉域水文地质特征。应用神经网络技术(ANN)建立泉域内县代表性的难老泉岩溶地下水位与各种补排项之间定量数学模型,对该泉域地下水可开采量进行了评价。研究结果表明,所建立的岩溶地下水位多因素神经网络模型具有较好的拟合精度,仿真程度较高,所得到的地下水可采资源量评价结果与该地区地下水开发利用实际情况较为一致。同时,还计算了不同降雨条件下地下水的可开采量,使其对地下水的开采规划更具有指导意义。 相似文献