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.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

EVAPOTRANSPIRATION DYNAMICS AT AN ECOHYDROLOGICAL RESTORATION SITE: AN ENERGY BALANCE AND REMOTE SENSING APPROACH1

ABSTRACT: Little work is reported where spatial methods are employed to monitor evapotranspiration (ET) changes as a result of vegetation and wetland restoration. A remote sensing approach with the Surface Energy Balance Algorithm for Land (SEBAL) for estimating ET at The Nature Conservancy's Glacial Ridge prairie‐wetland restoration site in northwestern Minnesota is presented. The calibrated 24‐hour ET from SEBAL was estimated with an average error of prediction of ?4.3 percent. Monthly, interseasonal, and seasonal ET for the period of June to September (2000 to 2003) from three adjacent land‐uses: a hydrologic control preserved wetland; a treated or restored site; and a nontreated or impacted wetland, were used in the study. Results from comparing ET behavior to the preserve suggest restoration efforts have affected monthly and seasonal ET within the treated site. Spatial average standard deviations of the seasonal ET within the preserve, treated, and nontreated sites give 47.3, 75.7, and 109.9 mm, respectively, suggesting hydrologic stabilization within the treated site. Monthly and interseasonal comparisons show similar behavior to that of the seasonal data, where monthly correlations suggest increasing agreement within the treated site, approaching those within the preserve.     

EFFECTS OF STATSGO AND SSURGO AS INPUTS ON SWAT MODEL'S SNOWMELT SIMULATION1

ABSTRACT: Soil data comprise a basic input of SWAT (Soil and Water Assessment Tool) for a watershed application. For watersheds where site specific soil data are unavailable, the two U.S. Department of Agriculture (USDA) soil databases, the State Soil Geographic (STATSGO) and Soil Survey Geographic (SSURGO) databases, may be the best alternatives. Although it has been noted that SWAT models using the STATSGO and SSURGO data may give different simulation results for water, sediment, and agricultural chemical yields, information is scarce on the effects of using these two databases in predicting streamflows that are predominantly generated from melting snow in spring. The objective of this study was to assess the effects of using STATSGO versus SSURGO as an input for the SWAT model's simulation of the streamflows in the upper 45 percent of the Elm River watershed in eastern North Dakota. Designating the model as SWAT‐STATSGO when the STATSGO data were used and SWAT‐SSURGO when the SSURGO data were used, SWAT‐STATSGO and SWAT‐SSURGO were separately calibrated and validated using the observed daily streamflows. The results indicated that SWAT‐SSURGO provided an overall better prediction of the discharges than SWAT‐STATSGO, although both did a good and comparable job of predicting the high streamflows. However, SWAT‐STATSGO predicted the low streamflows more accurately and had a slightly better performance during the validation period. In addition, the discrepancies between the discharges predicted by these two SWAT models tended to be larger at upstream locations than at those farther downstream within the study area.     

微波消解石墨炉原子吸收法测定空气中的锰(铅)

本文介绍了用微波消解技术消解滤膜以测定空气中的锰（铅）的方法，与传统的稀硝酸浸出法、流酸-灰化法相比，微波消解更加简单、快速、节省、准确、沾污少、环境污染少，回收率高。     

Designating Air Quality Management Areas (AQMAs) in the UK Implications for Securing UK Air Quality Objectives

Across the United Kingdom, the majority of local authoritieshave now completed their first phase of local air qualityreview and assessment work, as required under the AirQuality Strategy for England, Scotland, Wales and NorthernIreland (DETR, 2000a). Emerging from this first phasework is an anticipated suite of over 110 Air QualityManagement Areas (AQMAs). These areas are identifiedlocations where one or more of the national air qualityobjectives are predicted to exceed by specific target dates,and their spatial extent and shape is emerging as highlyvariable. Local authorities are guided to use a variety ofscientific tools to underpin the scientific assessments, anda consideration of uncertainty in both the tools used andsubsequent delineation of AQMAs is likely to affect theemerging management areas significantly. With subsidiarity underpinning the process of local air qualitymanagement (LAQM), local decision-making is anticipated toinfluence the outcome of the LAQM process in its entirety,with the declaration of AQMAs necessitating the preparationand implementation of air quality action plans. UKexperience of the effective management of local air quality,through the designation of AQMAs, demonstrates a valuableframework for other European countries developing mechanismsto manage air quality locally.     

Estimation of Cumulative Frequency Distribution for Carbon Monoxide Concentration from Wind-Speed Data, in Buenos Aires (Argentina)

In this article we apply and test a methodology to estimate cumulative frequency distribution for air pollutant concentration from wind-speed data. We use the inverse relationship after Simpson et al. (Atmospheric Environment, 19, 75–82, 1985) between the opposing percentile values in the statistical distributions for air pollutant concentrations and wind-speed data. This relationship is valid, irrespective of the statistical distributions of both variables, if an inverse relationship between them is also applicable. The available data are five years of 8-h average carbon monoxide concentration and 8-h mean wind-speed, observed in Buenos Aires (Argentina). The performance of the obtained empirical expressions in estimating cumulative frequency distributions for 8-h CO is statistically evaluated. The results show that it is possible to obtain an acceptable cumulative frequency distribution for 8-h CO concentration at the site if the cumulative frequency distribution for wind-speed is known. Q–Q plots show a good agreement between estimated and observed values. From our data, the mean relative error of the estimations was found to be as much as 8.0%.     

Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting

The last decade progress in numericalweather prediction (NWP) modelling and studies of urbanatmospheric processes for providing meteorological data forurban air pollution forecasting is analysed on examples ofseveral European meteorological centres. Modern nested NWP models are utilising land-use databasesdown to 1 km resolution or finer, and are approaching thenecessary horizontal and vertical resolution suitable forcity scale. The recent scientific developments in the fieldof urban atmospheric physics and the growing availabilityof high-resolution urban surface characteristics datapromise further improvements of the capability of NWPmodels for this aim. A strategy to improve NWP data forthe urban air pollution forecasting is suggested.     

Effects of Starch Moisture on Properties of Wheat Starch/Poly(Lactic Acid) Blend Containing Methylenediphenyl Diisocyanate

Methylenediphenyl diisocyanate was found to improve the interfacial interaction between poly(lactic acid)(PLA) and granular starch. The objective of this research was to study the effect of starch moisture content on the interfacial interaction of an equal-weight blend of wheat starch and PLA containing 0.5% methylenediphenyl diisocyanate by weight. Starch moisture (10% to 20%) had a negative effect on the interfacial binding between starch and PLA. The tensile strength and elongation of the blend both decreased as starch moisture content increased. At 20% moisture level, the starch granules embedded in the PLA matrix were observed to be swollen, resulting in poor strength properties and high water absorption by the blend.     

简易石灰石/石膏湿法烟气脱硫技术在连州电厂的应用

在介绍了连州电厂的广东省第一套简易石灰石／石膏湿法烟气脱硫（FGD）系统主要设计参数和工艺流程的基础上，重点对FGD系统在近2年的运动过程中出现的主要问题进行了分析，包括系统与锅炉的相互影响，烟道腐蚀问题，脱硫石膏与灰渣水的混排及吸收塔内搅拌器叶片的损坏等，同时从技术，经济两方面对该套FGD系统进行了总体的评价。