Artificial tracers for geothermal reservoir studies

Safe disposal of thermally spent geothermal brines that contain environmentally hazardous constituents is commonly obtained by reinjection. The reinjection process also serves to maintain reservoir pressure, enhance thermal recovery, and eliminate possible compactional subsidence. To avoid premature thermal breakthrough of reinjected fluids, tracer tests are employed for detection and evaluation of preferential path networks. In this paper some promising tracers that have not received much attention in geothermal reservoir studies are discussed, and a comprehensive tabulation of field sites of artificial tracer utilization is presented. Chemical and transport processes responsible for tracer retention by the formation of reservoir solids, as well as available tracer detection techniques, are emphasized.     

ARTIFICIAL INTELLIGENCE IN GEOGRAPHY: CONJECTURES ON THE SHAPE OF THINGS TO COME

Artificial intelligence (Al) and geography are brought together within a broad context involving fundamental issues of theory, epistemology and scientific method. The little known logico-mathematical background of Al is explored and shown to have substantial implications for modeling in general, prediction, the interpretation of theoretical structures, and our understanding of science itself. These more abstract aspects of the “computational revolution,”of which Al is one product, could have far-reaching consequences for geographic research.     

Integrating GIS and DSS for identification of suitable areas for artificial recharge,case study Meimeh Basin,Isfahan, Iran

Flood spreading is an inexpensive method for flood mitigation and artificial recharge of aquifers that results in a large budget return for relatively small investment.It is necessary to study some regional characteristics in order to determine the appropriate areas for artificial groundwater recharge by flood spreading in Meimeh Basin, Isfahan Province, Iran. Necessary regional characteristics to be studied are: slope, infiltration rate, sediment thickness, transmissivity, and water quality. In this research to identify suitable areas for artificial recharge several thematic layers were prepared, assigning each layer to one of the mentioned characteristics. The thematic layers were classified to several classes based on the existing criteria. All of the classes of the thematic layers were integrated and analyzed using a decision support system (DSS) in a geographical information system (GIS) environment. Finally suitability of the integrated classes for artificial recharge was identified in which the following classes were separated:(i) Very suitable, (ii) suitable, (iii) moderate suitability, and (iv) unsuitable.The validity of the generated model was verified by applying the model to a number of successful floodwater spreading stations throughout Iran. The verified model showed satisfactory results for all of the stations. The results for Meimeh Basin showed that about 70% of the Quaternary sediments in the studied area are suitable and moderately suitable for artificial recharge by flood spreading.     

U.K.USAGE OF CHEMOMETRICS AND ARTIFICIAL INTELLIGENCE IN QSAR ANALYSIS

A survey of members of the U.K.QSAR Discussion Group has been made to determine the extent ofuse and development of chemometric and artificial intelligence(AI)methods in the analysis ofmultivariate quantitative structure-activity relationship(QSAR)data in the U.K.Chemometric methodswere found to be well established in both industrial and educational establishments and there wassignificant method development occurring.AI methods were not employed to any great extent and thegeneral level of interest in these techniques was low compared to chemometric methods.A requirementfor more education in multivariate statistical methods and regression methods was indicated.A need fora user-friendly,comprehensive,commercially available multivariate statistical package containingmultivariate stability testing and regression diagnostic methods was identified.     

Canonical elements and Keplerian-like solutions for intermediary orbits of satellites of an oblate planet

Within the framework of the Canonical Formalism in the extended phase space,a general Hamiltonian is investigated that covers a wide class of radial intermediaries accounting for themajor secular effects due to a planet's oblateness perturbations.An analytical, closed-form solution for this generic Hamiltonian is developed in terms of elementary functions via the corresponding Hamilton-Jacobi equation. The analytical solution so obtained can be contemplated according to a simple geometrical and dynamical interpretation in Keplerian language by means of the usual relations characterizing elliptic elements along ahypothetic Keplerian motion.Appropriate choices for the terms appearing in the proposed Hamiltonian lead to recovering the analogues of some well-known, classical radial intermediaries (those introduced by Deprit and the one built by Alfriend and Coffey), but also certain new ones derived by Ferrándiz for the Main Problem in the Theory of Artificial Satellites of the Earth. In any case, the results are also applicable to problems dealing with orbital motion of other planetary satellites.The generality of this pattern leads to asystematic obtaining of solutions to the considered intermediaries: special choices of the Hamiltonian yield the correspondinganalytical solution to the respective intermediary problem.     

凝花菜的初步研究

对凝花菜的形态、结构、生长、繁殖；四分孢子的放散、附着及萌发；人工繁殖试验等进行了初步研究。     

人工神经网络预报模型的过拟合研究

针对神经网络方法在预报建模中存在的“过拟合”(overfitting)现象和提高泛化性能 (generalizationcapability)问题 ,提出了采用主成分分析构造神经网络低维学习矩阵的预报建模方法。研究结果表明 ,这种新的神经网络预报建模方法 ,通过浓缩预报信息 ,降维去噪 ,使得神经网络的预报建模不需要进行适宜隐节点数的最优网络结构试验 ,没有“过拟合”现象 ,并且与传统的神经网络预报建模方法及逐步回归预报模型相比泛化能力有显著提高     

Performance of geostatistical interpolation methods for modeling sampled data with non-stationary mean

The measured ozone pollution peak in the atmosphere of Mexico City region was considered in order to study the effect of a non-stationary mean of the sampled data in geostatistics interpolation methods. With this objective the local mean value of the sampled data was estimated through a linear regression analysis of their values on the monitoring station’s coordinates. The residuals obtained by removing the data trend are considered as a set of stationary random variables. Several interpolation methods used in geostatistics, such as inverse distance weighted, kriging, and artificial neural networks techniques were considered. In an effort to optimize and evaluate its performance, we fit interpolated values to sampled data, obtaining optimal values for the parameters defining the used model, that means, the values of the parameters that give the lowest mean RMSE between the interpolated value and measured data at 20 stations at 1500 hours for a set of 21 days of December 2001, which was chosen as the training set. The training set is conformed by all the days in December 2001 excepting the days (3,6,9,12,...,27,30) which were considered as the testing set. Once the optimal model is obtained, it is used to interpolate the values at the stations at 1500 hours for the testing days. The RMSE between interpolated and measured values at monitoring stations was also evaluated for these testing values and is shown as a percentage in Table 2. These values and the defined generalization parameter G, can be used to evaluate the performance and the ability of the models to predict and reproduce the peak of ozone concentrations. Scatter plots for testing data are presented for each interpolation method. An interpretation of the ozone pollution levels obtained at 1500 hours at December 21 was given using the wind field that prevailed in the region 1 h before the same day.     

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.     

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.