Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

A non-linear perturbation model considering catchment wetness and its application in river flow forecasting

A non-linear perturbation model for river flow forecasting is developed, based on consideration of catchment wetness using an antecedent precipitation index (API). Catchment seasonality, of the form accounted for in the linear perturbation model (the LPM), and non-linear behaviour both in the runoff generation mechanism and in the flow routing processes are represented by a constrained nan-linear model, the NLPM-API. A total of ten catchments, across a range of climatic conditions and catchment area magnitudes, located in China and in other countries, were selected for testing daily rainfall-runoff forecasting with this model. It was found that the NLPM-API model was significantly more efficient than the original linear perturbation model (the LPM). However, restric tion of explicit nan-linearity to the runoff generation process, in the simpler LPM-API form of the model, did not produce a significantly lower value of the efficiency in flood forecasting, in terms of the model efficiency index R-2. (C) 1997 Elsevier Science B.V.

基于InCoME的软件成本估算方法及支撑工具

软件成本估算领域经过四十余年的发展，涌现出一大批估算理论与方法，但都没有在现实环境中的软件企业中得到广泛应用，在项目早期进行软件成本估算仍旧是一件非常难的任务。由于估算模型的复杂性，缺乏相关的自动化的支撑工具及在现实软件企业中进行成本估算的应用研究是造成这一局面的一个重要原因。 本文从解决现实估算问题出发，提出了基于支撑工具的软件成本估算应用的具体框架。使用专家知识初始化模型，收集历史数据进行模型校准，并使用jack-knife交叉验证对模型进行精度分析。在建立可接受的模型后，收集待估算项目规模和成本因子数据，基于集成成本建模与估算（InCoME）方法，提供COCOMO、类比、回归等多种估算方法的支持。估算结果结合不确定性分析和风险分析，给项目计划和决策提供参考。成本估算应用框架形成一套完整和规范的流程，是一个现实可行的软件成本估算的解决方案。本文的另一贡献在于定制开发此应用框架的支撑工具，即集成成本建模与估算工具。在分析调研主流的软件成本估算工具的基础上，为配合估算应用框架，使用eclipse RCP和关系数据库，开发出层次清晰、可扩展性强、可维护性高、易升级易部署、界面友好的支撑工具。我们将基于支撑工具的软件成本估算应用框架应用于现实中的软件企业进行经验研究，经验研究结果表明，企业的软件成本估算得到了明显改善，支撑工具也被很好的接受。

InCoME软件成本估算工具

针对现行的成本估算软件存在算法支持不够、算法不公开、对建模支持不够等问题,提出了一种基于最优加权算法的集成软件成本估算方法InCoME,并且在此基础上采用Eclipse RCP框架、Java开发语言、HSQLDB关系数据库开发出了In-CoME成本估算软件。该软件不仅实现了驱动因子管理、集成模型支持、模型校准、模型精度分析等模块,而且还实现了基于不确定性的成本估算和风险分析功能,解决了国内一些企业急需成本估算软件等问题。

Deformation Monitoring and Forecasting Model Study Based on Grey System Theory

The grey system theory studies the uncertainty of small sample size problems. This paper using grey system theory in the deformation monitoring field, based on analysis of present grey forecast models, developed the spatial multi-point model. By using residual modification, the spatial multi-point residual model eras developed in further study. Then, combined with the sedimentation data of Xiaolangdi Multipurpose Dam, the results are compared and analyzed, the conclusion has been made and the advantages of the residual spatial multi-point model has been proved.

Application of forecasting structural cracks technique of 3D move in Chengdao buried hill

3DMove software, based on the three-dimension structural model of geologic interpretation, can forecast reservoir cracks from the point of view of formation of the structural geology, and analyze the characteristics of the cracks. 3DMove software dominates in forecasting cracks. We forecast the developments and directions of the cracks in Chengbei buried hill with the application of forecasting technique in 3DMove software, and obtain the chart about strain distributing on top in buried hill and the chart about relative density and orientation and the chart about the analysis of crack unsealing. In Chengbei 30 buried hill zone, north-west and north-east and approximately east-west cracks in Cenozoic are very rich and the main directions in every fault block are different. Forecasting results that are also verified by those of drilling approximately accord with the data from well logging, the case of which shows that the technique has the better ability in forecasting cracks, and takes more effects on exploration and exploitation of crack reservoir beds in ancient buried hill reservoirs.