Water resource management in the intermountain Izeh Plain, Southwest of Iran

Ever growing demand for water for agricultural activities in the Izeh Plain has enhanced the use of groundwater. Due to enormous groundwater abstraction since 1985, the overall static water level has receded by more than 5 meters reflecting that the aquifer is under stress condition. As a result, interest is focused on application of artificial recharge as an option for groundwater management to augment water supply in this area. Therefore, in the present investigations, suitable sites for artificial recharge were selected by an integrated surface and sub-surface assessment of the area. On the basis of the data collected from four target points, it was realized that the selected sites for artificial recharge could not meet water demand of the area. In this study attention was also paid to utilization of the existing Miangran Lake water as an alternative to combat water shortage for irrigation. The study further indicated that the available Miangran Lake water could be used for irrigation of the reclaimed agricultural land and enabling to convert 20o0 hectares of rain-fed land into irrigation. The total cost to utilize lake water is US$ 2,756,729 and it was estimated that the project could recoup the investment within 5 years which is quite reasonable in this water scarcity prone area.     

Prediction of significant wave height using regressive support vector machines

Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H_s). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H_s. Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.     

Artificial neural networks approach for zeta potential of Montmorillonite in the presence of different cations

In this study, the zeta potential of montmorillonite in the presence of different chemical solutions was modeled by means of artificial neural networks (ANNs). Zeta potential of the montmorillonite was measured in the presence of salt cations, Na⁺, Li⁺ and Ca²⁺ and metals Zn²⁺, Pb²⁺, Cu²⁺, and Al³⁺ at different pH values, and observed values pointed to a different behavior for this mineral in the presence of salt and heavy metal cations. Artificial neural networks were successfully developed for the prediction of the zeta potential of montmorillonite in the presence of salt and heavy metal cations at different pH values and ionic strengths. Resulting zeta potential of montmorillonite shows different behavior in the presence of salt and heavy metal cations, and two ANN models were developed in order to be compared with experimental results. The ANNs results were found to be close to experimentally measured zeta potential values. The performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance account for were used to control the performance of the prediction capacity of the models developed in this study. These indices obtained make it clear that the predictive models constructed are quite powerful. The constructed ANN models exhibited a high performance according to the performance indices. This performance has also shown that the ANNs seem to be a useful tool to minimize the uncertainties encountered during the soil engineering projects. For this reason, the use of ANNs may provide new approaches and methodologies.     

智能高效小型一体化泥浆不落地系统的研究与应用

针对固体矿产勘查钻探工程泥浆性能维护和固控装备自动化的需求,开展小型一体化泥浆不落地循环处理工艺研究和自动化智能化控制装备研制。装备主要由循环系统、净化系统、控制系统及循环管汇组成。系统引入PID控制和物联网技术,实现过渡泵的自反馈控制和远程可视化控制。现场应用结果表明,装备运行安全、可靠、平稳,净化效率高、自动化程度高,在技术、成本和环保等方面都取得了良好的效果,符合绿色勘查需求。通过自动化智能化控制技术,减少现场作业人员,降低工人劳动强度,一定程度上提升了岩心钻探设备自动化智能化水平,为实现岩心钻探设备自动化、智能化目标提供技术支撑。     

人工智能句法模式识别在物探资料解释中之应用

在物探资料解释中应用人工智能、句法模式识别还是最近的事情,本文是在这方面的一个尝试。讨论的内容有知识工程、数字图象处理、句法模式识别等。句法模式识别作为知识的某种形式包含于本文建造的位场资料解释智能初步系统之中。     

Embedding a Geographic Information System in a Decision Support System for Landslide Hazard Monitoring

In this paper we present an application that exploitsa geographic information system as a front-end of acomplex information system supporting the managementof landslide hazard in Valtellina, an alpine valley inNorthern Italy.A decision support system (EYDENET, operational sinceOctober 1996), incorporating a geographic informationsystem and a data interpreter based on artificialintelligence techniques, processes the readings of the250 most significant instruments of a monitoring netof about 1000 sensors installed on differentlandslides in several alpine valleys.Data gathered by extensometers, clinometers andpluviometers, to check both movements of rocks andclimatic conditions which could affect them, areprocessed by EYDENET, that provides on-lineinterpretation of data, helps the users analyse them,and generates natural language explanations and alarmmessages for the people responsible for theenvironmental management and the civil protection.     

三峡库区岩性智能分类研究

三峡库区岩体上方覆盖着厚实的土壤和茂密的植被,是高植被覆盖区,岩性信息弱,因此岩性识别和分类困难,没有成熟的方法可循。针对三峡库区进行岩性分析,选择三峡库区巴东城区作为研究区域,采用2000年5月成像的ETM+遥感影像,构造纹理、光谱、植被覆盖等17个分类因子,将遥感影像与地质图叠加,选取1 101个样本点,采用决策树C4.5算法,挖掘出三峡库区巴东县处岩性的解译规则和知识,决策树的学习精度为96.6%,剪枝后精度为95.9%,规则提取的精度为93.1%,提取的规则置信度很高,并基于知识驱动和规则匹配实现了岩性的智能分类,分类精度较高为90.11%;将分类结果与IsoData方法、K-Means方法、马氏距离法、最大似然法、最小距离法、平行六面体方法等6种方法的分类结果进行比较,试验结果证明,决策树方法的分类结果最好,精度明显高于其他6种方法。     

工程勘查钻进数据采集设备的自动控制系统设计与应用

本文阐述了工程勘查钻进数据采集设备的研制背景和性能要求。重点介绍了工程勘查钻进数据采集设备的主要结构和性能参数,工程勘查钻进数据采集设备的电气控制系统及重要电器元件选型。详细阐述了钻头输出恒定转速的自动闭环控制方案。最后经过施工现场试验,验证了工程勘查钻进数据采集设备能够输出恒定钻速,精确采集并存储钻头转速、扭矩、钻进速度、钻进压力等4个主要钻进参数。     

东莞地区土地利用变化预测的CBR和CA方法对比研究(英文)

Many studies on land use change(LUC),using different approaches and models,have yielded good results.Applications of these methods have revealed both advantages and limitations.However,LUC is a complex problem due to influences of many factors,and variations in policy and natural conditions.Hence,the characteristics and regional suitability of different methods require further research,and comparison of typical approaches is re-quired.Since the late 1980s,CA has been used to simulate urban growth,urban sprawl and land use evolution successfully.Nowadays it is very popular in resolving the LUC estimating problem.Case-based reasoning(CBR),as an artificial intelligence technology,has also been employed to study LUC by some researchers since the 2000s.More and more researchers used the CBR method in the study of LUC.The CA approach is a mathematical system con-structed from many typical simple components,which together are capable of simulating complex behavior,while CBR is a problem-oriented analysis method to solve geographic problems,particularly when the driving mechanisms of geographic processes are not yet understood fully.These two methods were completely different in the LUC research.Thus,in this paper,based on the enhanced CBR model,which is proposed in our previous research(Du et al.2009),a comparison between the CBR and CA approaches to assessing LUC is presented.LUC in Dongguan coastal region,China is investigated.Applications of the im-proved CBR and the cellular automata(CA) to the study area,produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR,and 70.7% accuracy from the CA.From the results,we can see that the accuracies of the CA and CBR approaches are both >70%.Although CA method has the distinct advantage in predicting the urban type,CBR method has the obvious tendency in predicting non-urban type.Considering the entire ana-lytical process,the preprocessing workload in CBR is less than that of the CA approach.As such,it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.     

一个圈闭评价专家系统的设计与实现

本文详细论述一个圈闭评价专家系统的设计与实现技术,该系统能够接收用户提供的圈闭信息,根据知识库中的专家知识给出圈闭的含油气资源评价;系统采用了基于黑扳的控制结构和基于规则的知识表达以及演绎推理、模糊推理、模糊综合评判等一系列技术在IBM-PC机上用C语言及少量汇编语言实现。系统已通过专家鉴定。