基于GBDT的实时洪水预报误差校正方法

洪水预报实时校正是提高预报精度的有效途径。通过研究实时洪水预报误差系列构建方法,引入GBDT方法建立误差校正模型,并采用粒子群算法优选模型参数,选用洪峰段洪量相对误差、洪峰流量相对误差、确定性系数等指标评估实时校正效果。对淮河流域王家坝站点的实例应用结果表明,无论是率定期还是验证期,基于GBDT的实时预报误差校正方法精度均优于经典AR方法和KNN方法,各项指标精度均有不同程度提升,可有效提高实时洪水预报效果,且稳定性较高。     

漳河水库入库洪水预报方案研究

在分析漳河水库入库洪水误差特点的基础上．建立了入库洪水产汇流预报方案。并进一步采用自适应实时校正技术，使本次制作的漳河水库洪水预报方案平均确定性系数达到91．09％，洪峰合格率为100％，峰现时间合格率为90．91％。     

基于集合卡尔曼滤波的实时校正技术在流域洪水预报中的应用

为提高金华江流域实时洪水预报精度，建立了耦合MIKE 11 NAM与MIKE 11 HD的流域洪水预报模型和基于集合卡尔曼滤波的实时校正模型，实现了金华江流域洪水预报实时校正。流域洪水预报模型对流域内主要站点的模拟效果较好，洪水流量和洪水水位模拟精度较高；实时校正模型在预见期10 h以内，校正效果随预见期增加而降低，在预见期前期可有效降低预报误差。整体上，建立的流域洪水预报模型和基于集合卡尔曼滤波的实时校正模型能够满足金华江流域洪水预报应用要求。     

基于误差自回归的洪水实时预报校正算法的研究

根据三水源新安江模型洪水预报误差信息，探讨了三种基于误差自回归模型的洪水实时预报校正算法，即固定遗忘因子的递推最小二乘算法，可变遗忘因子的递推最小二乘算法和辅助变量法，并将其应用于鲇鱼山水库的实时洪水预报。通过对三种实时校正方法进行分析比较，认为具有可变遗忘因子递推最小二乘算法效果最好。     

递推最小二乘与误差自回归联合实时校正方法

在实时洪水预报中采用速推最小二乘法和误差自回归实时校正方法，提高了模型计算精度，实例验证应用效果良好。     

SMG水文系统模型的实时校正

本文在分析讨论了具有物理成因概念的系统模型(SMG模型)误差和BP网络应用问题的基础上,对其加以改进建立了实时预报校正模型。将该模型应用于洮河流域红旗站日径流实时预报,结果显著地提高了预报方案的确定性系数,对系统模型的洪峰预报精度也有一定的提高。     

在实时水文预报系统中比较水文模型

提出了在实时洪水预报系统中比较水文模型的概念，涉及到概念性水文模型和数学模型．前者包含新安江模型（ＸＡＪ）、简化的新安江模型（ＳＸＡＪ）、ＦＧ模型．用湿润地区淮河流域资料在实时预报系统中对四个模型的预报结果进行比较．     

基于日需水量的作物非充分实时灌溉预报模型及应用

针对传统灌溉预报中实时降水信息与典型年降水信息差异较大导致预报决策在实际生产中失效的问题,在充分考虑预报日降水和天气类型情况下,基于实时的土壤水分监测数据和天气信息,提出了基于日需水量的作物非充分实时灌溉预报模型,给出了模型涉及的短期作物系数、土壤水分修正、计划湿润层深等参数的模拟与修正办法。以冬小麦为例,提出了不同天气类型下日需水量的计算方法,并进行了在线实时灌溉预报。结果表明,该预报模型与方法能为农业水资源发挥最大效益、有效缓解水资源短缺提供依据。     

松涛水库入库洪水预报流溪河模型研究

为准确、可靠地预报松涛水库入库洪水,基于流域降雨量和下垫面物理特性,探讨了分布式水文模型流溪河模型在松涛水库入库洪水预报中的适用性。结果表明,流溪河模型在松涛水库流域对场次洪水有较好的模拟效果,34场洪水模拟的确定性系数均值达0.869,洪峰误差均值为0.1。以洪峰流量的20%作为许可误差,松涛水库预报方案合格率为79%,预报方案精度达到乙级,说明该模型可应用于松涛水库入库洪水预报。研究成果可为松涛水库防洪调度提供技术支持。     

洪水扩散波实时水位预报模型及其算法

运用洪水扩散波理论建立了河道水水位实时预报模型，改进了Ｔ．Ｋ．Ｆｏｒｔｃａｃｎｅ时变遗忘因子最不二乘估计时变参数的递推算法，经淮河河段的洪水实时水模拟预报应用，表明所建立的模型和实时递推算法对于河道洪水水位短期预报是有效的。     

短期洪水预报的变结构神经网络模型

针对神经网络洪水预报模型的结构难以确定的问题，应用一种在训练过程中可调整隐层神经元数的算法，建立了变结构神经网络洪水预报模型。该方法提供了设计面向问题的网络结构的途径，在网络结构设计，提高洪水预报精度等方面具有一定的实用性。     

Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather‐dependent variable, namely, degree days (DD). The third model is also a multivariate model based on EEC and a gross domestic product (GDP) proxy, namely, total imports (TI). Finally, the fourth model combines EEC, DD and TI. Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for all models. Copyright © 2002 John Wiley & Sons, Ltd.     

Neural network approach to estimate 10-min solar global irradiation values on tilted planes

Calculation of solar global irradiation on tilted planes from only horizontal global one is particularly difficult when the time step is small. We used an Artificial Neural Network (ANN) to realize this conversion at a 10-min time step. The ANN is developed and optimized using five years of solar data and the accuracy of the optimal configuration is around 9% for the RMSE and around 5.5% for the RMAE i.e. similar or slightly lower than the errors obtained with empirical correlations available in the literature and used for the estimation of hourly data.     

梯级水电站洪水优化调度几个问题的探讨

本文详细讨论文献[1]中梯级水电站洪水优化调度线性二次型控制模型分解协调算法的收敛性,推导出收敛的充分条件.针对预报入流过程存在误差,提出实时决策的基本思路,建立了上游电站洪水优化调度方案的实时修正公式,用以处理洪水最优化调度方案所需计算时间与洪水调度实时性要求之间的矛盾。     

Performance and exhaust emissions of a gasoline engine using artificial neural network

This study deals with artificial neural network (ANN) modelling of a gasoline engine to predict the brake specific fuel consumption, brake thermal efficiency, exhaust gas temperature and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a four-cylinder, four-stroke test engine was fuelled with gasoline having various octane numbers (91, 93, 95 and 95.3), and operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back-propagation algorithm for the engine was developed. Then, the performance of the ANN predictions were measured by comparing the predictions with the experimental results which were not used in the training process. It was observed that the ANN model can predict the engine performance, exhaust emissions and exhaust gas temperature quite well with correlation coefficients in the range of 0.983–0.996, mean relative errors in the range of 1.41–6.66% and very low root mean square errors. This study shows that, as an alternative to classical modelling techniques, the ANN approach can be used to accurately predict the performance and emissions of internal combustion engines.     

Fuzzy modeling and particle swarm optimization for determining the optimal operating parameters to enhance the bio-methanol production from sugar cane bagasse

This work aims to maximize the production of bio-methanol from sugar cane bagasse through pyrolysis. The maximum value of the bio-methanol yield can be obtained as soon as the optimal operating parameters in a pyrolysis batch reactor are well defined. Using the experimental data, the fuzzy logic technique is used to build a robust model that describes the yield of bio-methanol production. Then, Particle Swarm Optimization (PSO) algorithm is utilized to estimate the optimal values of the operating parameters that maximize the bio-methanol yield. Three different operating parameters influence the yield of bio-methanol from sugar cane bagasse through pyrolysis. The controlling parameters are considered as the reaction temperature (°C), reaction time (min), and nitrogen flow (L/min). Accordingly, during the optimization process, these parameters are used as the decision variables set for the PSO optimizer in order to maximize the yield of bio-methanol, which is considered as a cost function. The results demonstrated a well-fitting between the fuzzy model and the experimental data compared with previous predictions obtained by an artificial neural network (ANN) model. The mean square errors of the model predictions are 0.11858 and 0.0259, respectively, for the ANN and fuzzy-based models, indicating that fuzzy modeling increased the prediction accuracy to 78.16% compared with ANN. Based on the built model, the PSO optimizer accomplished a substantial improvement in the yield of bio-methanol by 20% compared to that obtained experimentally, without changing system design or the materials used.     

Estimation of solar radiation components incident on Helwan site using neural networks

Field testing carried out for solar energy applications is costly, time consuming and depends heavily on prevailing weather conditions. Adequate security and weather protection must be provided at the test site. Measurements may also suffer from delays that can be caused by system failures and bad weather. To overcome these problems the need for accurate model becomes evermore important. To achieve such prediction task, an artificial neural network, ANN, model is regarded as a cost-effective technique superior to traditional statistical methods. In this paper, Levenberg optimization function is adopted to predict insolation data in different spectral bands for Helwan (Egypt) monitoring station. The predicted values were then compared with the actual values and presented in terms of usual statistics. The results hint that, the ANN model predicted infrared, ultraviolet, and global insolation with a good accuracy of approximately 95%, 93% and 96%, respectively. In addition, ANN model was tested to predict the same components for Aswan over an 11 month period. The predicted values of the ANN model compared to the actual values for Aswan produced an accuracy of 95%, 91% and 92%, respectively. Data for Aswan were not included as a part of ANN training set. Hence, these results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates.     

Modelling of a cascade refrigeration system using artificial neural network

This study investigates the applicability of artificial neural networks (ANNs) to predict various performance parameters of a cascade vapour compression refrigeration system. For this aim, an experimental cascade system was set up and tested in steady‐state operating conditions. Then, utilizing some of the experimental data for training, an ANN model for the system based on the standard back propagation algorithm was developed. The ANN was used for predicting the evaporating temperature in the lower‐temperature circuit, compressor power for the lower and higher circuits and coefficients of performance for both the lower circuit and the overall cascade system. Afterwards, the performances of the ANN predictions were tested using new experimental data. The ANN predictions usually agreed well with the experimental results with correlation coefficients in the range of 0.953–0.996 and mean relative errors in the range of 0.2–6.0%. Furthermore, the ANN yielded acceptable predictions for the system performance outside the range of the experiments. The results suggest that the ANN approach can alternatively and reliably be used for modelling cascade refrigeration systems. Copyright © 2006 John Wiley & Sons, Ltd.     

结合改进雨量不确定性计算方法的洪水概率预报及其应用

为更准确地预报洪水发生概率,针对传统雨量不确定性计算方法中相对误差估计不准确的问题,将独立同分布中心极限定理引入降雨不确定性计算中,推求一定区域内某次降水过程中面雨量测值的相对偏差、测量误差以及相对误差,实现降雨不确定性概率描述;降雨量概率分布计算与确定性水文预报模型耦合,最终实现考虑降雨不确定性的洪水概率预报,并以滩坑流域2014年间5场洪水过程为例对该方法进行了验证。结果表明,5场洪水预报的确定性系数均在0.89以上,洪峰误差均在9%以内,洪量误差均在7%以内,且预报区间覆盖率均在61%以上。说明结合改进雨量不确定性计算方法的洪水概率预报效果较好,预报精度和覆盖率高,具有一定工程实际意义。     

Modelling of a direct evaporative air cooler using artificial neural network

This paper proposes the use of artificial neural networks (ANNs) to predict various performance parameters of a direct evaporative air cooler. For this aim, an experimental evaporative cooler was operated at steady‐state conditions, while varying the dry bulb temperature and relative humidity of the entering air along with the flow rates of air and water streams. Using some of the experimental data for training, a three‐layer feed‐forward ANN model based on back propagation algorithm was developed. This model was used for predicting various performance parameters of the cooler, namely the dry bulb temperature and relative humidity of the leaving air, mass flow rate of the water evaporated into the air stream, sensible cooling rate, and effectiveness of the cooler. Then, the performance of the ANN predictions was tested by applying a set of new experimental data. The predictions usually agreed well with the experimental values with correlation coefficients in the range of 0.969–0.993, mean relative errors in the range of 0.66–4.04%, and very low root mean square errors. This study reveals that, as an alternative to classical modelling techniques, the ANN approach can be used successfully for predicting the performance of direct evaporative air coolers. Copyright © 2007 John Wiley & Sons, Ltd.