Water Inflow Forecasting Research Articles

Assessing Hydrological Response in the Timah-Tasoh Reservoir Sub-Catchments: Calibration and Validation using the HEC-HMS Model

Hydrological modelling is a tool that is frequently used for assessing the hydrological response of a basin as a result of precipitation. It is also a vital component as water resources and environmental planning management. The study deals with calibrating and validating the hydrological response in the sub-catchments of the Timah-Tasoh reservoir using the hydrological model named Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS). This study uses the SCS Curve Number, the SCS Unit Hydrograph, the constant monthly baseflow, and lag routing for the model development. The model was simulated for ten (10) years for calibration and nine (9) years for validation. The model calibration and validation efficiency were assessed using the coefficient of correlation (R). The findings show that the HEC-HMS model performs satisfactorily in simulating the observed daily inflow series, with the R-value of 0.4902-0.5139 during calibration and 0.5047-0.5559 during validation process. Thus, the result obtained from this study can be used as a preliminary development of hydrological modelling of the catchment of the Timah-Tasoh reservoir and can be used for extend application such as water inflow forecasting, impact of land use to the reservoir and others.

Data Processing Technology for the Forecasting of the Water Inflow into a Reservoir with the Use of Earth Remote Sensing and the Network of Meteorological and Hydrological Posts

Management of the hydropower plants requires the economically efficient use of water resources based on the forecasts and simulation models of the hydropower plant and the reservoir. There are various data sources for the water inflow forecasting: meteorological and hydrological posts, Earth remote sensing. However, the problem arises of combining the specified heterogeneous data for aggregated processing with the use of machine learning methods. The research goal is to design an architecture of a system for collecting and processing the data from various sources to operational forecast of the water inflow and the reservoir water-level. It was achieved by analyzing and selecting the sources and methods for the use of Earth remote sensing data; observing the main principles of hydrological modeling; assessing the availability of the different data; analyzing the ways of increasing the observability of the hydrological objects by installing additional meteorological and hydrological posts; and designing a technology for the automatic data collection and processing. The most significant results are developed architecture of the data collection and processing system and the technology for aggregating heterogeneous data with the use of machine learning methods. It is aimed to reduce the error of short-term forecasting of the water inflow to the reservoir. The significance of the results lies in the fact that the proposed technology was offered and justified for a real hydropower plant; and it can improve the water resources management efficiency: increase the energy generation, minimize the sterile spills, increase the flood forecasting horizon and reduce the risk of flooding during the spring high water.

Development of Medium-Term Water Inflow Forecasting Models for Planning Electricity Generation in Isolated Power Systems

Reliable operation of power systems with a significant share of hydropower plants (HPPs) in the energy mix depends in many respects on how accurately the water inflow is forecasted. Taken together, the water inflow prediction and optimal planning of production define energy security, ensure the possibility of protection from floods, and eliminate idle discharges at hydroelectric power plants. The solution of such problems is complicated by lack of reliable information about the water inflow, its being stochastic in nature, a variable electricity consumption pattern, and inaccurate prediction and planning models. Improvement of prediction accuracy is focused on determining the water inventory for planning prospective electricity generation at HPPs taking into account regulation in the medium term. Such regulation is necessary to meet the power system load in the load curve peak and semi-peak parts. The paper considers the problem of constructing a medium-term water inflow prediction model for planning electricity generation for a week ahead taking into account climate changes in isolated operating power systems taking as an example the electric power systems of the Gorno-Badakhshan autonomous oblast in Tajikistan. For taking into account constant climate changes, it is proposed to use an approach based on machine learning methods, which features a self-adaptation capability. Based on the results of accomplished experimental and industry-grade numerical analyses, the expediency of using a model based on an ensemble of regression decision trees has been shown.

Interval uncertainty analysis of a confined aquifer

Water inflow forecast is influenced by many factors and yields uncertain results. To more accurately predict the magnitude of water inflow and quantitatively define the corresponding response in the parameter change interval, this study combined a non-probabilistic set theory and uncertainty analysis to derive an equation for the confined water inflow. Using mining area data and comparing the calculation of upper and lower boundary limits obtained by a Monte Carlo method, results of the confined water inflow equation were calculated with relative errors of 5% and 10%. When corresponding to the rate of change of the variable parameter, the results showed that under the same error conditions, the allowable rate of change when calculating the minimum value using Eq. A was greater than when using Eq. B, and the maximum value using Eq. B yielded a greater allowable rate of change than the maximum value calculated by Eq. A. Thus, the obtained rate of change for Eq. A is indicative of the lower limit, and Eq. B is conducive to the calculation of the upper limit of mine water inflow.

A Sequential Structure for Water Inflow Forecasting in Coal Mines Integrating Feature Selection and Multi-Objective Optimization

To construct an accurate and stable approach for water inflow forecasting, a series of advanced and effective techniques, such as variational mode decomposition (VMD), outlier robust extreme learning machine (ORELM) and multi-objective grey wolf optimizer (MOGWO), are appropriately integrated into this study. Considering that the influence of the mode number on the VMD decomposition effectiveness, such an argument is determined by observing the converged centre frequency distribution among the components. Then the characteristic items of water inflow series are extracted by VMD, thus obtaining a series of sub-components. Afterwards, ORELM is applied to predict each component, where the parameters of ORELM are optimized by MOGWO with multi-objective functions including forecasting accuracy and stability. Correspondingly, the aggregation of all components’ prediction values is considered as the final results. The experimental results obtained by performing eight various models on real-time data demonstrate that the supplementary modules achieve positive effects on the improvement of prediction accuracy, where the proposed model implements an average performance promotion of 48.43% compared with the contrastive models.

井下探放水钻孔涌水量优化预测及水量衰减规律研究

以钱营孜煤矿3212工作面探放水工程为例，针对目前煤矿安全生产中“钻孔涌水量预测”的重要问题，在分析计算不同阶段和条件下的矿井涌水量的基础上，对涌水量计算结果的准确性与群孔探放钻孔涌水量干扰进行分析，研究工作面涌水量衰减规律。研究表明：采用大井法优化方法预计探放水过程中钻孔涌水量，较原计算方法更合理，更符合地下水运动规律。在同一个钻窝内进行多个钻孔的探放时，相邻钻孔的初始水量有减小的趋势。以静储量为主的煤系砂岩裂隙水，随着疏放探放水钻孔的涌水量总体呈下降趋势。 Taking QianYingZi coal mine 3212 working face of draining project as an example, specific to the important issue of “borehole water inflow forecast” during the present situation of coal mine safety production, based on the analysis and calculation of mine water inflow in different stages and conditions, we research the attenuation law of water inflow on the working face by analysing the accuracy on the calculation results of the inflow and the interference of the group of exploration boreholes inflow. Researches show that: the big well method is more reasonable than the original calculation method in process of the optimization to drain hole water in flow and it is more in line with groundwater movement rule. For multiple drilling exploration in the same drill socket, adjacent borehole of initial water has a tendency to decline. Give priority to those with static reserves of coal measures hole as a whole, it is on the decline.

Analysis on Hydrogeological Conditions of Nanlizhuang Iron Ore in China

In allusion to mining design of succedent filling method on heavy-water deposit of Nanlizhuang iron ore, based on engineering geology, hydrogeological conditions of the mining area are studied. Through geological structure, strata and deposit hydrogeological condition were surveyed, analyzed source of water filling and main way of the ore deposit, and determined hydrogeology parameters of the mining area. It provided the theoretical basis for mining design and water inflow forecast, and will lay the foundation for safety production and production capacity of the mine.

Application of Numerical Simulation in the Analysis of Water Supply Stability of Power Plant

Currently, the mine water inflow is often forecasted by using the large diameter well method, but under complicated hydrogeological conditions where Dupuit well flow formula is not suitable, the said method is not applicable. The paper takes an example of the water inflow forecast of Chaokewula Coal Mine and discusses the solution of adopting the numerical simulation software Visual Modflow: the simulation scope is defined by analyzing the hydrogeological conditions of Chaokewula Coal Mine to set a mathematical model that fits the actual situation. The model can be identified and verified based on the set model boundary conditions, model spatial discretization, initial conditions and simulation period. Forecast the water inflow of each working face in 30 years by using the underground water numerical simulation software Visual MODFLOW so as to provide a basis for the stability estimation of the mines power plant water supply. Keywords: Numerical Simulation, Water Inflow, Power Plant Water Supply

Optimization of Mine Drainage Capacity Using FEFLOW for the No. 14 Coal Seam of China’s Linnancang Coal Mine

The hydrogeology of China’s Linnancang coal mine is very complicated. The aquifuge that lies between the no. 14 coal seam and the underlying K3 limestone aquifer is less than 20 m thick and the mine contains 10 known faults. The largest water inrush event exceeded 9.25 m3/min. Comprehensive modeling of the seepage field was undertaken to determine the appropriate water level in neighboring aquifers to reduce the likelihood of water inrush events. A 3-D hydrogeological model of the coal seam and associated aquifers was established to determine the characteristics of aquifers in the seepage field. The results, along with analysis of the existing mining conditions, showed that water levels will rise, but that if four dewatering wells were to be added in the mine area, the water levels would markedly decline. The water inflow forecast also showed that it is necessary to pump water and simulate water levels under different pumping pressure conditions. Based on this work, it appears that increasing the present drainage capacity by 30 % is economical and feasible.

Generation risk assessment in volatile conditions with wind, hydro, and natural gas units

This paper studies a generating company (GENCO)’s midterm (a few months to a year) scheduling payoffs and risks in volatile operating conditions. The proposed algorithm considers the integration of intermittent wind units into a GENCO’s generation assets and coordinates the GENCO’s hourly wind generation schedule with that of natural gas (NG) units (with volatile gas prices) and hydro units (with water inflow forecast) for maximizing the GENCO’s payoff. The proposed midterm GENCO model applies market price forecasts to the risk-constrained stochastic price-based unit commitment (PBUC) for calculating the GENCO’s risk in energy and ancillary services markets. The proposed PBUC minimizes the cost of (a) NG contracts, storage, startup and shutdown, (b) startup and shutdown of cascaded hydro units, and (c) penalty for defaulting on the scheduled power delivery. Simulation results show that the diversification of generating assets including bilateral contracts (BCs) could enhance the GENCO’s midterm planning by increasing the expected payoff and decreasing the financial risk.

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

Improvements to the water management of a run-of-river HPP reservoir: methodology and case study

The operational planning for run-of-river hydroelectric power plants with small storage capacities is directly linked to the natural inflow of the reservoir water. An ability to accurately forecast the natural inflow can result in the increased production of electric energy as a result of an enhanced flexibility in the management of the stored water. In this article a novel methodology for building an efficient short-term water-inflow forecaster using neural network techniques is presented. The emphasis is given to a precise definition of the problem and the tailoring of the problem-solving requirements to specific case conditions. The proposed methodology was applied to the River Soča cascade hydroelectric system. The efficiency of the algorithm was evaluated in terms of the reduction in the amount of water spillage and the enhanced production-planning accuracy. Finally, some results that justify the development of the short-term water-inflow forecasting algorithm are presented.

Enhanced Artificial Neural Network Inflow Forecasting Algorithm for Run-of-River Hydropower Plants

An improved artificial neural network-based algorithm for short-term water inflow forecasting (STWIF) into run-of-river hydropower plants is presented. Deregulation of the electrical power industry and introduction of competition between power producers have prompted redefinition of their daily operational tasks. In a competitive market environment, accurate short-term production planning and profitable bidding strategies become an important issue, requiring water inflow forecasts for up to 36 h ahead. As a result, short forecasting horizons have been found to be a main drawback of first-generation STWIF. An additional module using forecast precipitation data is developed for enlarging the forecast horizon up to two days ahead. The water inflow forecaster is further enhanced by inclusion of new input variables. With a large number of potential input variables, a new algorithm for selection of input variables using average mutual information and nonparametric density estimation is applied to the specific problem of water inflow forecasting. The performance of the enhanced STWIF is applied to the Soca River cascade hydropower system in Slovenia, and results are presented along with some comparisons with the previous-generation STWIF.

Neural-network-based water inflow forecasting

Water inflow forecasting is usually based on precipitation data collected by the ombrometer stations in the river basin. Solution of this problem is rather complex, due to the highly non-linear relation between the amount of precipitation at different locations and the water inflow into the head hydro power plant reservoir. In this paper, a new approach to forecasting water inflow, based on neural networks, is presented. First, selection of input parameters is discussed. Next, the most appropriate architecture of the neural networks, is chosen. Finally, the efficacy of the proposed method is tested for a practical case, and some results are presented.

Neural Network Based Water Inflow Forecasting

Water inflow forecasting is usually based on precipitation data collected by the ombrometer stations in the river basin. Solution of this problem is rather complex due to highly non-linear relation between the amount of precipitation at different locations and water inflow. In the paper, a new approach to forecasting water inflow into the head hydro power plant reservoir based on neural networks is described. First, selection of input parameters is discussed. Next, the most appropriate architecture of the neural network is chosen. Finally, efficacy of the proposed method is tested for a practical case and some results are presented.