The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

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.

Analysis of AI-based techniques for forecasting water level according to rainfall

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.