Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models
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The uncertainty research of hydrological forecast attracts the attention of a host of hydrological experts. Prediction Interval (PI) is a convinced method that can ensure the forecasting accuracy meanwhile take uncertainty range into consideration. While the existed Prediction Interval methods need algorithm optimization and are susceptible to local optima, so it is particularly urgent to provide an efficient Prediction Interval (PI) model with excellent performance. This paper proposes a novel upper and lower bound interval estimation model to rapidly define the PI and reduce the amount of calculation to implement convenient and high precise hydrological forecast. Above all, the ideal upper and lower bounds are defined according to the relative width or absolute width. Then, the proposed model is utilized to forecast interval runoff via least square method and multiple linear regression methods. The estimated interval inclusion ratio, interval width, symmetry, and root-mean-square error which are popular used to judge the precision serve as accuracy evaluation indexes. The measured discharge data from five hydrological stations which located upstream of the Yangtze River is applied for interval forecasting. Compared with the results of neural network-based upper and lower bound interval estimation model, the proposed method yields higher forecasting accuracy, meanwhile, the ideal upper and lower bounds successfully minimize the number of processes which require a mass of parameter searching and optimization.
KeywordsInterval hydrological forecasting The ideal boundary Multiple linear regression models Upper and lower bound estimation
This work was supported by the State Key Program of National Natural Science of China of Major Research Projects (No. 91547208), 2017 research project of ChongQing Municipal Education Commission “The cross-sectional study of optimal hydraulics in rural irrigation canals of Chongqing Three Gorges Reservoir Region” (KJ1735447), High-level talent introduction fund of Chongqing Water Resources and Electric Engineering Colleage in the year 2016-2017 (No.:KRC201702): Study on the Bearing Capacity of Region Water Resources—by Taking the YongChuan District in ChongQing City as an Example.
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- Fan FM, Schwanenberg D, Alvarado R, dos Reis AA, Collischonn W, Naumman S (2016) Performance of deterministic and probabilistic hydrological forecasts for the short-term optimization of a tropical hydropower reservoir. Water Resour Manag 1–17Google Scholar
- Kasiviswanathan KS, Sudheer KP (2016) Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models. Model Earth Syst Environ 2(1). https://doi.org/10.1007/s40808-016-0079-9
- Li M, Yang D, Chen J, Hubbard SS (2012) Calibration of a distributed flood forecasting model with input uncertainty using a Bayesian framework. Water Resour Res 48(8). https://doi.org/10.1029/2010WR010062
- Marshall L, Nott D, Sharma A (2004) A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling. Water Resour Res 40(2):n/a-n/a. https://doi.org/10.1029/2003wr002378