长江上游月降水人工神经网络预测模型

长江上游月降水量预测对于三峡库区及整个长江流域水资源管理具有重要意义。根据长江上游不同气候区降水差异,选取玉树、九龙和宜宾3个代表性气象站点近60 a的月降水量数据,运用反向传播神经网络、径向基函数神经网络、广义回归神经网络和多元线性回归法,确定降水时滞和降水月份,建立月降水预测模型,来预测未来一个月的降水量,并采用均方误差和判定系数来验证和对比各种模型的模拟效果。结果显示：人工神经网络模型总体上优于多元线性回归,特别是反向传播神经网络的模拟结果各站表现较好,在确定合理的输入变量和网络结构后,可以尝试作为长江上游各站月降水预测模型。〖     

基于广义加权组合预测法的建设用地需求预测——以苏州市为例

对现行采用的多种常用建设用地需求量预测方法进行了比较,并以苏州市为例,采用5种预测模型进行了对比分析,利用近13年的统计数据进行了验证.结果表明,广义加权组合预测法得到的结果误差最小、精度最高.     

A Simple Framework for Incorporating Seasonal Streamflow Forecasts into Existing Water Resource Management Practices1

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.     

Uncertainty Reduction of the Flood Stage Forecasting Using Neural Networks Model1

Abstract: The Elman Discrete Recurrent Neural Networks Model (EDRNNM), which is one of the special types of neural networks model, is developed and applied for the flood stage forecasting at the Musung station (No. 1) of the Wi‐stream catchment, which is one of the International Hydrological Program representative basins, Korea. A total of 135 different training patterns, which involve hidden nodes, standardization process, data length, and lead‐time, are selected for the minimization of the architectural uncertainty. The model parameters, such as optimal connection weights and biases, are estimated during the training performance of the EDRNNM, and we apply them to evaluate the validation performance of the EDRNNM. Sensitivity analysis is used to reduce the uncertainty of input data information of the EDRNNM. As the results of sensitivity analysis, the Improved EDRNNM consists of four input nodes resulting from the exclusion of Dongkok station (No.5) in initial five input nodes group of the EDRNNM. The accuracy of flood stage forecasting during the training and validation performances of the Improved EDRNNM remains the same as that of the EDRNNM. The Improved EDRNNM, therefore, gives highly reliable flood stage forecasting. The best optimal EDRNNM, so called the Improved EDRNNM, is determined by elimination of the uncertainties of architectural and input data information in this study. Consequently, we can avoid unnecessary data collection and operate the flood stage forecasting system economically.     

香港的空气污染指数预报

介绍了香港空气污染指数预报的方法和所取得的良好社会效果。通过香港空气污染指数预报的学习结合内地环境保护工作的情况，谈了几点体会。     

基于雨量(强)条件的泥石流预测预报研究现状、问题与建议

在对基于雨量(强)条件泥石流预测预报现状综合分析的基础上,提出了目前降雨型泥石流预测预报中存在的问题:①前期降雨对泥石流发生的贡献问题;②前期降雨对泥石流影响的衰减问题;③前期有效降雨天数的确定问题;④前期降雨和短历时降雨的权重衡量问题;⑤基于前期有效雨量的泥石流预测预报模式的确定问题。提出了降雨型泥石流预测预报程式框架和相关建议:①基于雨量(强)条件的泥石流预测预报应同机理研究密切结合;②单沟泥石流临界雨量条件和预测预报应同泥石流类型密切结合;③区域泥石流临界雨量条件和预测预报应同环境地质背景密切结合。     

福建省台湾入境游客市场预测模型分析

随着海峡两岸旅游区的发展,对台湾旅游市场进行预测可以更好地帮助福建省制定对台旅游发展战略和市场开发模式。通过对目前较为实用的两种预测模式——回归分析和灰色预测进行分析比较得出指数模型在游客预测中的优越性,从而得出相应的预测模型。对未来几年游客量进行预测,从定量分析的角度对福建省开展对台旅游提出依据。     

枣庄市水体污染现状及治理对策研究

对枣庄市水体环境污染现状进行了简要分析，对7条主要河流水质现状进行比较，运用指数平滑预测模型、灰色预测模型以及组合预测模型等三种预测分析方法对2010年枣庄市水环境污染状况进行了预测，在借鉴国外水污染治理成功经验的基础上，根据枣庄市水污染现状和成因，按照系统性、可持续发展、统一规划、分期实施的原则，研究了枣庄市水环境污染综合治理对策，提出从观念技术创新（发展循环经济等）、体制机制改革（进行水资源管理体制改革等）、公共财政投入（工程设施、资金保障等）等方面进行枣庄市的城市水环境污染的治理工作。     

中国火灾最佳灰色回归组合预测模型

火灾现象具有随机性、模糊性,是个复杂的灰色系统行为。研究火灾发生的规律及发展趋势,具有实用价值。笔者给出最小二乘估计意义下的最佳组合预测模型的定义,并求得其权的公式和证明权的惟一性;用回归分析方法建立多个回归模型,并按3条标准即①回归指数大、②系统误差小、③模型精度高,选定最佳非线性回归模型;用灰色理论建立多个灰色模型,并按3条标准即①后验差比值小、②小误差概率大、③预测关联度大,选定最佳灰色模型;再将最佳回归模型与最佳灰色模型有机地结合起来建立中国火灾最佳灰色回归组合预测模型。组合预测模型综合利用前两者提供的不同的有用信息,改善了单一模型的局限性,提高了预测精度,减少了预测误差。组合模型预测中国年火灾起数处于动态增长过程。     

MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

Abstract: An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydro-logically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks station (RR-GF) in North Dakota. The modeling exercise used 1 year (2002), 5 years (1998–2002), and 27 years (1975–2002) of data for the daily, weekly, and monthly predictions, respectively. The hydrometeorological data (precipitations P_(t), P_(t-1), P_(t-2), P_(t-3), antecedent runoff/lake stage R_(t-1) and air temperature T_(t) were partitioned for training and for testing to predict the current hydro-graph at the selected DL and RR-GF stations. Performance of ANN was evaluated using three combinations of daily datasets (Input I = P_(t)), P_(t-l), P_(t-2), P_(t-3), T_(t) and R_(t-l); Input II = Input-l less P_(t) P_(t-l), P_(t-2), P_(t-3); and Input III = Input-II less T_(t)). Comparison of the model output using Input I data with the observed values showed average testing prediction efficiency (E) of 86 percent for DL basin and 46 percent for RR-GF basin, and higher efficiency for the daily than monthly simulations.