中国工业二氧化硫排放量的灰色预测新模型

中国工业二氧化硫排放量既是一个灰色系统，又是一个离散动态系统。本文基于ＧＰＭ（１）灰色生长曲线，建立了预测中国工业二氧化硫排放量的ＧＩＰＭ（１）这一灰色对数幂函数曲线新模型。应用表明，ＧＩＰＭ（１）不仅与实际具有良好的一致性，也为国家制定工业二氧化硫排放量的控制治理规划提供了理论依据。     

IS支持下水质模型的应用

地理信息系统（ＧＩＳ） 是集计算机科学、地球科学、信息科学为一体的高新技术。目前,ＧＩＳ技术已广泛用于资源管理、环境监测、环境评价、灾害评估、区域流域环境规划等众多领域,已成为国内外环境管理的有效决策支持工具。本文介绍了ＧＩＳ技术在金华江流域水污染控制决策方面的应用, 其中重点介绍了在ＧＩＳ支持下,金华江流域水污染决策模型（ 水质模型） 的建立、程序设计和实现     

雨水中S（IV）浓度和氧化速率的测定

本文介绍了日本横滨雨水中Ｓ（ＩＶ）浓度和氧化速率的测定方法：Ｓ（ＩＶ）在雨水中的氧化反应一组反应；Ｆｅ＾３＋和Ｍｎ＾２＋离子对雨水中Ｓ（ＩＶ）的氧化有强的催化作用；Ｓ（ＩＶ）的浓度是０．８～２３．５μｍ和氧化率常数０．１２～３．３小时＾－１。     

最佳实用技术

ＧＧＴＩ型燃煤锅炉烟气脱硫器ＧＧＴＩＦｌｕｅＧａｓＤｅｓｕｌｆｕｒｉｚｅｒｆｏｒＣｏａｌ－ｆｕｅｌｅｄＢｏｉｌｅｒ由重庆爱曼迪实业公司开发、重庆市环境保护局推荐的ＧＧＴＩ型燃煤锅炉烟气脱硫器，适用于２～２２０ｔ／ｈ燃煤锅炉（蒸汽炉、沸腾炉）。该...     

文摘

文摘▲《大气腐蚀对气候钢ＡＴＭＯＦＩＸ５２Ａ机械性能的影响》——《Ｃｏｒｏｓ．Ｓｃｉ》１９９３，Ｖ３５，ｎ１－４，Ｐｔ．１Ｐ７１９－７２５（英）：对暴露于农村、工业和城市地区０．５～１０年的试样，调查了其中的气候钢ＡＴ－ＭＯＦＩＸ５２Ａ的腐蚀动力学、...     

第63届IEC年会介绍

１　概况第６３届ＩＥＣ年会于１９９９年１０月１７日～２９日在日本京都国际会馆举行。出席会议的代表来自５２个国家约１４００多人，其中中国代表４８人。会议期间，６０多个技术委员会或分技术委员会分别举行了工作会议和各自的工作组会议，对不同专业领域的国际标准草案进行了认真细致的讨论，并确定了今后的工作计划。在此期间大会组委会还组织到ＯＭＲＯＮ（欧姆龙）、Ｋｙｏｃｅｒａ、Ｍａｔｓｕｓｈｉｔａ（松下）、Ｓｕｍｉｔｏｍｏ（住友）、Ｓｈａｒｐ（夏普）等日本著名企业进行参观和技术交流。此外，会议期间还安排了一场Ｉ…     

半挥发性有机物GC/MS分析方法研究

用气相色谱／质谱（ＧＣ／ＭＳ）研究分析了环境中的半挥发性有机标准物。以十氟三苯基磷作调机物,对ＧＣ／ＭＳ仪器系统进行了调整,离子丰度值符合美国环保局（Ｕ．Ｓ．ＥＰＡ）１９８６年推荐的离子丰度标准。测定了３２种目标化合物的保留时间及相对保留因子ＲＦ值,结果表明除五氯苯酚不达标外,其余化合物的ＲＦ值均符合要求,３１种目标化合物的加标回收率为２１％～９７％,相对标准偏差为３．１％～２４．１％,符合美国环保局８２７０方法质量控制检验标准。最后对建立定量校正库与ＧＣ／ＭＳ分析进行了讨论     

西南地区卫星林火监测系统的建立

本文对建立ＧＩＳ支持的ＮＯＡＡ卫星林火监测系统进行了分析．内容包括：监测区ＧＩＳ的形成；ＮＯＡＡ卫星图像叠加经纬度网格；ＧＩＳ与ＮＯＡＡ卫星图像的复合．１９９４年春天，我们对西南林区林火进行了１个多月的实时监测，证明了该系统具有林火分辨率高和定位准确的特点．是一个较为理想的林火监测系统。     

新型除尘滤袋材料—P84及其复合滤料

新型除尘滤袋材料──Ｐ＿（８４）及其复合滤料ＮＥＷ　ＦＩＬＴＥＲＢＡＧＭＡＴＥＲＩＡＬ──Ｐ＿（８４）ＡＮＤＩＴＳＣＯＭＰＯＳＩＴＥＦＩＬＴＥＲＩＮＧＭＥＤＩＵＭＡｎｅｗｆｉｌｔｅｒｂａｇｍａｔｅｒｉａｌ──Ｐ＿（８４）ａｎｄｉｔｓｃｏｍｐｏｓｉｔｅ...     

唐山清源环保机械（集团）公司

唐山清源环保机械（集团）公司ＴＡＮＧＳＨＡＮＱＩＮＧＹＵＡＮＥＮＶＩＲＯＮＭＥＮＴＡＬＣＯＮＴＲＯＬＥＱＵＩＰＭＥＮＴ（ＧＲＯＵＰ）ＣＯＲＰＯＲＡＴＩＯＮＴａｎｇｓｈａｎＱｉｎｇｙｕａｎＥｎｖｉｒｏｎｍｅｎｔａｌＣｏｎｔｒｏｌＥｑｕｉｐｍｅｎｔ（Ｇｒ...     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

NLGM（1，1^s,t）模型的建立与应用

运用灰色系统理论,建立了一种非线性GM（l,l^s,t）灰色新模型,并将其应用于某厂SO2排放量的预测之中。结果表明,NLGM（l,l^s,t）具有良好的模型品质,中长期预测定GM（l,l）更可靠。     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

APPLICATION OF GREY MODEL TOWARD RUNOFF FORECASTING1

ABSTRACT: A reliable forecasting model is essential in real‐time flood forecasting for reducing natural damage. Efforts to develop a real‐time forecasting model over the past two decades have been numerous. This work applies the Grey model to forecast rainfall and runoff owing to the model's relative ability to predict the future using a small amount of historical data. Such a model significantly differs from the stochastic and deterministic models developed previously. Ten historical storm events from two catchment areas in northern Taiwan are selected to calibrate and verify the model. Results in this study demonstrate that the proposed models can reasonably forecast runoff one to four hours ahead, if the Grey error prediction method is further used to update the output of the model.     

基于灰色神经网络的能源消费组合预测模型

组合预测对于信息不完备的复杂经济系统具有一定的实用性。鉴于能源消费系统的复杂性和非线性特征，利用我国能源消费的历史数据，采用灰色预测的GM（1，1）、无偏GM（1，1）和pGM（1，1）3种模型与人工神经网络进行优化组合，建立了灰色神经网络的能源消费组合预测模型，实证分析结果获得了更为精确的预测效果，可以作为能源消费预测的有效工具。同时，能源消费的预测结果也表明今后必须以节能为主导思想，努力建设资源节约型社会和环境友好型社会。     

灰色预测模型GM（1,1）在地面水质预测中的应用

本文将地面水质视作灰色系统,对某河段的化学耗氧量(COD),生化耗氧量(BOD_5)用灰色预测模型GM(1,1)进行建模、预测。最大误5.2％,平均误差小于0.2％,模型精度高。建模需要的数据较少,对数据的分布无特殊要求,方法和计算简单,实用价值大,是地面水质预测预报的一种有效方法。     

FORECAST UNCERTAINTY IN WATER SUPPLY RESERVOIR OPERATION1

ABSTRACT: This research investigates the benefits of forecasting in water supply systems. Questions relating operational losses to forecast period and accuracy are addressed. Some simple available forecasting techniques are assessed for their accuracy and applicability. These issues are addressed through the use of a simulation model of the Cedar and South Fork Tolt Rivers, where the system is modeled as a single purpose reservoir supplying municipal and industrial water to the Seattle metropolitan area. The following conclusions were made for this system: (1) reservoir operation deteriorates markedly with the loss of forecast accuracy; (2) the optimal length of forecasting period is five months; (3) reservoir operation may be improved by as much as 88 percent if perfect predictive abilities are available; (4) the mean of the historic data is not recommended to predict future flows because Markov methods are always superior; and (5) lag-one autoregressive Markov schemes exhibit about a 9 percent improvement in operation over no forecasting.     

System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts

Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.