Slake durability study of shaly rock and its predictions   总被引：2，自引：0，他引：2  

T. N. Singh  A. K. Verma  Vasudev Singh  A. Sahu 《Environmental Geology》2005,47(2):246-253

More than 35% of the earths crust is comprised of clay-bearing rocks, characterized by a wide variation in engineering properties and their resistance to short term weathering by wetting and drying phenomenon. The resistance to short-term weathering can be determined by slake durability index test. There are various methods to determine the slake durability indices of weak rock. The effect of acidity of water (slaking fluid) on slake durability index of shale in the laboratory is investigated. These methods are cumbersome and time consuming but they can provide valuable information on lithology, durability and weather ability of rock. Fuzzy set theory, Fuzzy logic and Artificial Neural Networks (ANN) techniques seem very well suited for typical complex geotechnical problems. In conjunction with statistics and conventional mathematical methods, a hybrid method can be developed that may prove a step forward in modeling geotechnical problems. During this investigation a model was developed and compared with two other models i.e., Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and artificial neural network system, for the prediction of slake durability index of shaly rock to evaluate the performance of its prediction capability.  相似文献   

Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks   总被引：1，自引：0，他引：1  

Hatim Elhatip  M. Aydin Kömür 《Environmental Geology》2008,53(6):1157-1164

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   

ARIMA和ANN模型的干旱预测适用性研究          下载免费PDF全文

杨慧荣  张玉虎  崔恒建  高峰  陈秋华 《干旱区地理》2018,41(5):945-953

开展干旱预测是有效应对干旱风险的前提基础,根据1960-2016年三江平原7个站点逐日降水和气温数据,利用ARIMA和ANN模型对不同时间尺度标准化降水蒸散指数（SPEI）序列进行分析建模预测。借助相关系数R、纳什效率系数NSE、Kendall秩相关系数τ、均方误差MSE和Kolmogorov-Smirnov （K-S）检验对模型的有效性进行了判定,然后分别用ARIMA和ANN模型进行12步预测,并将预测值与实际值进行比较。结果表明：（1） ARIMA模型和ANN模型对SPEI的预测能力都随时间尺度的增加而逐渐提高。（2）两种模型对3、6个月尺度SPEI的预测精度偏低,9、12、24个月的SPEI的预测精度在70%以上;（3）SPEI-9、SPEI-12、SPEI-24三个时间尺度ANN模型的预测精度优于ARIMA模型。  相似文献   

非平稳时间序列的动态水位神经网络预报模型   总被引：5，自引：0，他引：5  

薛联青  崔广柏  陈凯麒 《湖泊科学》2002,14(1):19-24

水文预报系统是一个复杂的非线性动力学过程,站点水位受各种因素的影响不仅呈现出非平稳动态随机变化特性,而且各因素间的关系也很难确定。淮河流域五河站水位由于受到洪泽湖回水影响及季节性的影响,也呈现出这一动力学的非平稳特性,因此本文在考虑了相关站点和回水影响的基础上,建立了一种多站变量时间序列的神经网络预报模型,预报结果表明该方法预测效果较好,运行简单。  相似文献   

Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed   总被引：1，自引：0，他引：1  

E. Mutlu  I. Chaubey  H. Hexmoor  S. G. Bajwa 《水文研究》2008,22(26):5097-5106

This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R² and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R² values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R² values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

基于人工神经网络的地下水状态方程—以山东莱州湾南岸地下水研究为例   总被引：2，自引：1，他引：1  

韩非  薛禹群  张永祥 《高校地质学报》2001,7(4):483-487

山东莱州湾南岸地下水密度受多种因素影响,传统方法难以拟合,利用人工神经网络（简称ANN）高效的自组织学习能力和抗实验噪音能力及应用神经网络工具箱设计了三层反馈式神经网络模型,得出映射莱州湾南岸地下水密度与含盐量之间相关关系的基于ANN的地下水状态性关系的性质,基于ANN的地下水状态方程具有形式简单,易于使用等特点。  相似文献   

Hydrological model comparison and assessment: criteria from catchment scales and temporal resolution     

C. Gao  M.T. Yao  J.Q. Zhai  S. Buda  T. Fischer 《水文科学杂志》2013,58(10):1941-1951

ABSTRACT

This study examines the performance of three hydrological models, namely the artificial neural network (ANN) model, the Hydrologiska Byråns Vattenbalansavdelning-D (HBV-D) model, and the Soil and Water Integrated Model (SWIM) over the upper reaches of the Huai River basin. The assessment is done by using databases of different temporal resolution and by further examining the applicability of SWIM for different catchment sizes. The results show that at monthly scale the performance of the ANN model is better than that of HBV-D and SWIM. The ANN model can be applied at any temporal scale as it establishes an artificial precipitation–runoff relationship for various time scales by only using monthly precipitation, temperature and runoff data. However, at daily scale the performance of both HBV-D and SWIM are similar or even better than the ANN model. In addition, the performance of SWIM at a small catchment size (less than 10 000 km²) is much better than at a larger catchment size. In view of climate change modelling, HBV-D and SWIM might be integrated in a dynamical atmosphere-water-cycle modelling rather than the ANN model due to their use of observed physical links instead of artificial relations within a black box.