Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Marko...Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.展开更多
Flood control forecast operation mode is one of the main ways for determining the upper bound of dynamic control of flood limited water level during flood season. The floodwater utilization rate can be effectively inc...Flood control forecast operation mode is one of the main ways for determining the upper bound of dynamic control of flood limited water level during flood season. The floodwater utilization rate can be effectively increased by using flood forecast information and flood control forecast operation mode. In this paper, Dahuofang Reservoir is selected as a case study. At first, the distribution pattern and the bound of forecast error which is a key source of risk are analyzed. Then, based on the definition of flood risk, the risk of dynamic control of reservoir flood limited water level within different flood forecast error bounds is studied. The results show that, the dynamic control of reservoir flood limited water level with flood forecast information can increase the floodwater utilization rate without increasing flood control risk effectively and it is feasible in practice.展开更多
Water level forecasting according to rainfall is important for water resource management and disaster prevention.Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such a...Water level forecasting according to rainfall is important for water resource management and disaster prevention.Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the area.Recently,with the improvement of AI(Artificial Intelligence)technology,a research using AI technology in the water resource field is being conducted.In this research,water level forecasting was performed using an AI-based technique that can capture the relationship between data.As the watershed for the study,the Seolmacheon catchment which has the rich historical hydrological data,was selected.SVM(Support Vector Machine)and a gradient boosting technique were used for AI machine learning.For AI deep learning,water level forecasting was performed using a Long Short-Term Memory(LSTM)network among Recurrent Neural Networks(RNNs)used for time series analysis.The correlation coefficient and NSE(Nash-Sutcliffe Efficiency),which are mainly used forhydrological analysis,were used as performance indicators.As a result of the analysis,all three techniques performed excellently in water level forecasting.Among them,the LSTM network showed higher performance as the correction period using historical data increased.When there is a concern about an emergency disaster such as torrential rainfall in Korea,water level forecasting requires quick judgment.It is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.展开更多
Koyna region, a seismically active region, has many time series observations such as seismicity, reservoir water levels, and many bore well water levels. One of these series is used to predict others since these param...Koyna region, a seismically active region, has many time series observations such as seismicity, reservoir water levels, and many bore well water levels. One of these series is used to predict others since these parameters are interlinked. If these series were stationary, we used correlation analysis. However, it is seen that maximum of these time series are nonstationary. In this case, co-integration method is used that is extracted from econometrics and forecast is possible. We have applied this methodology to study time series of reservoir water levels of this region and we find them to be co-integrated. Therefore, forecast of water levels for one of the reservoir is done from the other as these will never drift apart too much. The outcomes demonstrate that a joint modelling of both data sets based on underlying physics resolves to be sparingly useful for understanding predictability issues in reservoir induced seismicity.展开更多
基金supported by the National Natural Science Foundation of China (50879085)the Program for New Century Excellent Talents in University(NCET-07-0778)the Key Technology Research Project of Dynamic Environmental Flume for Ocean Monitoring Facilities (201005027-4)
文摘Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51079015, 50979011)
文摘Flood control forecast operation mode is one of the main ways for determining the upper bound of dynamic control of flood limited water level during flood season. The floodwater utilization rate can be effectively increased by using flood forecast information and flood control forecast operation mode. In this paper, Dahuofang Reservoir is selected as a case study. At first, the distribution pattern and the bound of forecast error which is a key source of risk are analyzed. Then, based on the definition of flood risk, the risk of dynamic control of reservoir flood limited water level within different flood forecast error bounds is studied. The results show that, the dynamic control of reservoir flood limited water level with flood forecast information can increase the floodwater utilization rate without increasing flood control risk effectively and it is feasible in practice.
文摘Water level forecasting according to rainfall is important for water resource management and disaster prevention.Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the area.Recently,with the improvement of AI(Artificial Intelligence)technology,a research using AI technology in the water resource field is being conducted.In this research,water level forecasting was performed using an AI-based technique that can capture the relationship between data.As the watershed for the study,the Seolmacheon catchment which has the rich historical hydrological data,was selected.SVM(Support Vector Machine)and a gradient boosting technique were used for AI machine learning.For AI deep learning,water level forecasting was performed using a Long Short-Term Memory(LSTM)network among Recurrent Neural Networks(RNNs)used for time series analysis.The correlation coefficient and NSE(Nash-Sutcliffe Efficiency),which are mainly used forhydrological analysis,were used as performance indicators.As a result of the analysis,all three techniques performed excellently in water level forecasting.Among them,the LSTM network showed higher performance as the correction period using historical data increased.When there is a concern about an emergency disaster such as torrential rainfall in Korea,water level forecasting requires quick judgment.It is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.
文摘Koyna region, a seismically active region, has many time series observations such as seismicity, reservoir water levels, and many bore well water levels. One of these series is used to predict others since these parameters are interlinked. If these series were stationary, we used correlation analysis. However, it is seen that maximum of these time series are nonstationary. In this case, co-integration method is used that is extracted from econometrics and forecast is possible. We have applied this methodology to study time series of reservoir water levels of this region and we find them to be co-integrated. Therefore, forecast of water levels for one of the reservoir is done from the other as these will never drift apart too much. The outcomes demonstrate that a joint modelling of both data sets based on underlying physics resolves to be sparingly useful for understanding predictability issues in reservoir induced seismicity.
