LINEAR PROGRAMMING APPLICATIONS IN WATER RESOURCES1

Applications of linear programming to water quality and water quantity problems are discussed, and a fairly comprehensive sample of recent literature in these areas is reviewed. Basic elements of linear programming are also discussed. Emphasis is placed on the elements of linear programming that make it a useful tool for analyzing water resource problems and the basic features of various water resource problems that render them amenable to meaningful analysis by linear programming.     

DETERMINING SIGNIFICANCE AND PRECISION OF ESTIMATED PARAMETERS FOR RUNOFF FROM SEMIARID WATERSHEDS1

ABSTRACT Significant parameters for predicting thunderstorm runoff from small semiarid watersheds are determined using data from the Walnut Gulch watershed in southern Arizona. Based on these data, thunderstorm rainfall is dominant over watershed parameters for predicting runoff from multiple linear regression equations. In some cases antecedent moisture added significantly to the models. A technique is developed for estimating precision of predicted values from multiple linear regression equations. The technique involves matrix methods in estimating the variance of mean predicted values from a regression equation. The estimated variance of the mean predicted value is then used to estimate the variance of an individual predicted value. A computer program is developed to implement these matrix methods and to form confidence limits on predicted values based on both a normality assumption and the Chebyshev inequality.     

THE DEVELOPMENT OF A MODEL TO EVALUATE HYDROLOGIC RISK IN WATER RESOURCE SYSTEM DESIGN1

In this paper, a procedure for analyzing a water resource system with special emphasis on evaluation of acceptable economic risk due to occasional failures to deliver water is proposed. The basic methodology includes the development of a simple mathematical model which describes the physical hydrologic and economic characteristics of a single reservoir irrigation and city water supply system and an evaluation of economic benefits of the system with full and partial deliveries of water. The system is simulated for various combinations of decision variables (system magnitudes) and an optimum design is obtained by response surface technology. Emphasis is placed on the basic model and methodology although, in order to introduce some realism, the procedure is applied to data based on the existing reservoir system on the South Concho River in West Central Texas.     

MODEL STUDIES OF SALT WATER INTRUSION1

Model studies were and are still being used to verify certain theories in ground-water flow systems in general. In complex cases, the model studies may be extremely useful especially when a theoretical rigorous analysis does not exist. The models cannot be considered entirely satisfactory due to the several drawbacks in each type in addition to the normal human errors in experimentation. This paper is concerned only with the viscous flow models. However, a brief summary of the other types of models, which may possibly be used in connection with salt water intrusion problems is given. It should be noted that some of such experiments are not directly related to the field of salt water intrusion. Two main types lie within this category: The gravity flow systems which are analogous to some phases of salt intrusion problems and problems in oil fields which bear general similarities to sea water intrusion zones. In oil fields, gas cycling studies give valuable information to sea water problems. Model studies are used by hydraulic engineers, geologists, petroleum engineers, physicists, foundation engineers and several other professional groups.     

废水生化处理出水吸收SO2过程相平衡关系的确定

应用相平衡理论对吸收液－ＳＯ２体系的相平衡关系进行了系统研究,确定了吸收相平衡的数学模型,并通过试验对数学模型进行了验证,结果表明,数学模型计算值与试验实测值基本吻合。     

新建水库初期磷氮变化的动态模拟研究

本文首先建立磷氮时空分布模型并确定模型参数的率定方法。文章模拟了新建清平水库建库初期磷氮变化情况,得出由不稳定到稳定的过程。水库磷氮时空变化的模拟结果表明,空间分布是从库尾到大坝浓度逐渐递砬,时间分布是一年中Ｐ,Ｎ浓度七月份最高,年初年末最低,其分布央线类似高斯分布。     

Water Supply Planning Considering Uncertainties in Future Water Demand and Climate: A Case Study in an Illinois Watershed

Ensuring an adequate, reliable, clean, and affordable water supply for citizens and industries requires informed, long-range water supply planning, which is critically important for water security. A balance between water supply and demand must be considered for a long-term plan. However, water demand projections are often highly uncertain. Climate change could impact the hydrologic processes, and consequently, threaten water supply. Thus, understanding the uncertainties in future water demand and climate is critical for developing a sound water supply plan. In Illinois, regional water supply planning attempts to explore the impacts of future water demand and climate on water supply using scenario analyses and hydrologic modeling. This study is aimed at developing a water supply planning framework that considers both future water demand and climate change impacts. This framework is based on the Soil and Water Assessment Tool to simulate the watershed hydrology and conduct scenario analyses that consider the uncertainties in both future water demand and climate as well as their impacts on water supply. The framework was applied to water supply planning efforts in the Kankakee River watershed. The Kankakee River watershed model was calibrated and validated to observed streamflow records at four long-term United States Geological Survey streamflow gages. Because of the many model parameters involved, the calibration process was automated and was followed by a manual refinement, resulting in good model performance. Long-range water demand projections were prepared by the Illinois State Water Survey. Six future water demand scenarios were established based on a suite of assumptions. Climate scenarios were obtained from the Coupled Model Intercomparison Projection Phase 5 datasets. Three representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, are used in the study. The scenario simulation results demonstrated that climate change appears to have a greater impact on water availability in the study area than water demand. The framework developed in this study can also be used to explore the impacts of uncertainties of water demand and climate on water supply and can be extended to other regions and watersheds.     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction

ABSTRACT

In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective.