The invention discloses a hydrological
data analysis and forecasting method based on
machine learning, which comprises the following steps: acquiring a
digital elevation model of a target area, and determining an
earth surface confluence direction based on the
digital elevation model; obtaining shrinkage crack trend information of the target area and determining a crack diversion direction; acquiring a rainfall
time sequence and river channel monitoring time sequences of the first river channel and the second river channel; inputting the rainfall
time sequence, the riverway monitoring
time sequence of the first riverway, the riverway monitoring time sequence of the second riverway, the
earth surface confluence direction and the crack diversion direction into a cross-
catchment area water delivery judgment model, and outputting a cross-basin
water delivery judgment result; and when the inter-basin
water delivery judgment result represents that inter-basin water delivery exists, the effective rainfall of the rainfall event is redistributed from the basin corresponding to the first river channel to the basin corresponding to the second river channel, and a forecast
water level or forecast flow is generated respectively. According to the method, the problems of false report and missing report caused by cross-basin hidden water delivery under the shrinkage crack condition are solved.