Overcoming flood and desiccation made by implementing artificial recharge toward absorption reservoir

Flood and desiccation are perceived as two most critical and influential disasters which contradict between causes and consequences. Flood occurs when the water surface could no longer flow the whole water flow, thus, the water flooded. Contradicted to that, desiccation occurs when the water flow contains a low volume of water deposit, thus, the water requirement exceeds the available potential. That condition was caused by land utilization as the consequences due to the increased land requirement for housing or industrial needs. An attempt to overcome the flood, nowadays, is implemented mostly in a structural way, through building canals, implementing rivers normalization, building gateways or building flood control pump which are more directed toward the flood direction in order to increase the surface flow in an immediate maner to the sea. However, the effort to overcome the flood its self, could be more effective if followed by an effort to increase the soil ability to absorb natural recharge or artificial recharge or by refilling the water into the earth surface. Absorption reservoir used as one of technology alternatives （artificial recharge） could also be used to support the attempt to overcome flood and desiccation. Absorption reservoir is a dam which was designed according to the basic principles, such as the bottom surface of the reservoir that has a high permeability surface; the surface of the reservoir water is higher （higher aquifer） along with a high permeability, considering the availability of water source that has been absorbed and its quality; considering the aquifer category on the water absorption dam;     

反扩散格式在二维锋生数值模拟中的应用

把无粘流中能成功捕捉接触间断的反扩散差分格式，用在了准地转二维理想锋生的数值模拟中，并将计算结果与Cullen的计算结果和半地转方程的精确解进行了比较。用反扩散格式模拟的锋面较Cullen的结果更加显，而且更接近半地转方程的精确解。     

吐鲁番山区人工增水作业效果的统计分析

应用统计检验和回归分析两种数理统计方法，对吐鲁番1997－2000年山区人工增水作业效果进行了分析。结果得出作业影响区内作业年份5－6月平均流量显著增加。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

Simulation of artificial random field considering the temporal-spatial variation

Introduction For the seismic design of special structures such as nuclear power station, marine platform, long-span bridge and dam, generally the time-history response analysis of the structure under seismic excitation is imperative, which was coded in most seismic design codes. The earthquake records suitable for the seismic situation and site condition are necessary to be used as the seismic input in the dynamic analysis of structures. As a result of the limited observational condition of st…     

Application of genetic BP network to discriminating earthquakes and explosions

Introduction Artificial Neural Network (ANN) is an important branch of artificial intelligence. It is proposed on the foundation of the study on modern neural science, is a man-made network that can implement some functions based on the mans comprehensive understanding for cerebral neural network (HAN, WANG, 1997). ANN is a mathematical model of simplified human brain neural network and is used to simulate the structures and functions of human brain neural network. ANN is a complex netw…     

湖北牛山湖高体鳑鮍的年龄、生长与繁殖

图 3　卵粒大小的频数分布 (ａ)具成熟卵细胞 (ｂ)无成熟卵细胞Ｆｉｇ 3ＦｒｅｑｕｅｎｃｙｄｉｓｔｒｉｂｕｔｉｏｎｏｆｅｇｇｓｉｚｅｏｆＲｈｏｄｅｕｓｏｃｅｌｌａｔｕｓ(ａ)ｗｉｔｈｓｏｍｅｍａｔｕｒｅｅｇｇｓ ,(ｂ)ｗｉｔｈｏｕｔｍａｔｕｒｅｄｅｇｇｓ图 4繁殖力 (Ｆ ,ｅｇｇｓ)与体重 (Ｗ ,ｇ)及全长 (ＴＬ ,ｍｍ )的关系Ｆｉｇ 4Ｒｅｌａｔｉｏｎｓｈｉｐｂｅｔｗｅｅｎｆｅｃｕｎｄｉｔｙ (Ｆ ,ｅｇｇｓ)ａｎｄｂｏｄｙｗｅｉｇｈｔ (Ｗ ,ｇ)ｏｒｔｏｔａｌｌｅｎｇｔｈ (ＴＬ ,ｍｍ)湖北牛山湖高体鳑鮍的年龄、生长与繁殖@…     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.     

沙坡头人工植被固沙区天然降水的入渗和分配研究

土壤、植被冠层与大气界面间(SVAT)物质传输过程日益成为水文学研究最感兴趣的领域,降水量的迁移与转换是非灌溉区SVAT主要的物质传输过程.干旱半干旱区稀疏灌丛蒸散量占降水量的90%以上,因此对降水入渗与水分在土壤内植物根际区再分配的研究显得十分重要.试验于2001年8月17日至9月30日在中国科学院沙坡头试验站进行,主要观测人工植被荒漠灌木柠条(Caragana korshinskii) 灌丛固定沙丘降水入渗与再分配过程.结果表明:在7次不连续降水过程中,土壤入渗深度与降水强度呈简单线性相关关系,土壤入渗速率约为降水强度的10倍.当次降水过程中降水强度小于0.46 mmh-1时,土壤入渗速率约为0 cm*h-1,此时的降水对沙区土壤基本上没有水分补给作用.受荒漠灌木柠条根系吸水作用的影响,其根系密集剖面深度40～140 cm内降水水分入渗积累不明显.降水入渗速率及入渗深度受土壤剖面初始含水率多寡而变化,干燥土壤剖面有助于提高瞬时入渗速率.降水以后随着时间的推移,人工固沙区微环境内空气温度、湿度等气象条件适宜,柠条生长进入相对旺盛阶段,其根系密集层140 cm深度处土壤含水率在总体上下降的过程中,表现出昼消夜长的趋势,试验期间翌日 8:00时土壤含水率值略高于前一日20:00时水分值 0.1%～0.3%.