Patent ID: 11886967
Assignee: WUHAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 0:
1. A long-term streamflow forecast method based on process-data synergic drive, comprising following steps:
step 1, collecting streamflow data, meteorological data, climate system indices, climate model forecasts, watershed elevation data, soil data and vegetation data;
step 2, constructing a daily-scale Variable Infiltration Capacity (VIC) distributed hydrological model of a target watershed and performing calibration and validation for model parameters;
step 3, after spatially interpolating the climate model forecasts to a grid scale matching the VIC distributed hydrological model, firstly using a linear scaling method to perform bias correction on the climate model forecasts, and then using a k-nearest neighbor algorithm-based model to disaggregate monthly-scale meteorological data forecasted by a climate model into daily data;
step 4, by using the spatially-interpolated and disaggregated climate model forecasts, driving the VIC distributed hydrological model to perform a gridded runoff yield calculation within a calibration period;
step 5, with observed monthly streamflow values of an outlet hydrological station within the calibration period as dependent variables and with monthly data obtained by aggregating runoff yield outputs of all grids within a watershed in different lead times as independent variables, selecting a third layer of soil moistures with the runoff yield outputs as daily scale, and using a LASSO regression model to perform feature variable screening, and then taking grids corresponding to the screened-out runoff yield outputs as typical grids to construct an improved VIC model; the step 5 comprising the following sub-steps:
sub-step 5.1, extracting observed daily streamflow values of the outlet hydrological station within the calibration period and the runoff yield outputs of all grids within the watershed in different lead times; aggregating both the streamflows and the runoff yield outputs into monthly data and then performing normalization to eliminate a dimensional influence between variables;
sub-step 5.2, with the monthly streamflow values as dependent variables and with the runoff yield outputs of the grids as independent variables, using the LASSO regression model to perform feature variable screening, wherein a target function set in the form of least square is, min
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 in the formula, x and y correspond to the independent variables and the dependent variables respectively, m corresponds to a dependent variable series length, n corresponds to a number of grids of the target watershed, β0 is a constant item, β is a coefficient of each variable, λ is a penalty parameter; taking the grids corresponding to the screened-out runoff yield outputs as typical grids;
step 6, driving the improved VIC model with the disaggregated climate model forecasts to perform the gridded runoff yield calculation of a full time period;
step 7, re-aggregating daily-scale gridded runoff yield calculation results of the improved VIC model into monthly-scale data and combining the monthly-scale data with historical streamflows, the climate model forecasts, the meteorological data and the climate system indices to form a candidate predictor set of a data-driven model, wherein different lag times are to be considered for the influence of the historical streamflows, the meteorological data and the climate system indices on the forecast monthly streamflows;
step 8, performing normalization on the candidate predictor set and the monthly streamflow data respectively and screening predictors by using a machine learning model; then, training a deep learning model using the screened-out predictors to further obtain a composite model based on process-data synergic drive, and using the composite model to perform long-term streamflow forecast.