7 results about "Rainfall index" patented technology

The invention discloses a debris flow early warning judgment method and system, and belongs to the technical field of hydraulic engineering, and the method comprises the following steps: a, determining the gully bed longitudinal gradient and the area of a drainage basin formation region of potential gully-type debris flow through a topographic map; b, obtaining lithology types of gully-type debris flow, and classifying the lithology types of the gully-type debris flow; c, calculating topographic indexes of gully-type debris flow; d, calculating a rainfall index for inducing gully-type debris flow; e, calculating an early warning discrimination index of the gully-type debris flow; and f, according to the early warning discrimination indexes of the gully-type debris flow, dividing early warning levels of the gully-type debris flow with different lithology. According to the method, a quantitative comprehensive early warning judgment index is constructed, the scientificity and accuracy of judgment of the debris flow starting critical condition are remarkably improved, multi-stage dynamic early warning from extremely low risk to high risk is achieved, and a direct operable technical basis is provided for accurate implementation of graded emergency response and disaster prevention resource allocation.

The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to rainfall prediction technical field.The application discloses a kind of research area seasonal rainfall prediction method and system, it relates to

The application discloses a kind of mountain torrent disaster dynamic early warning index determination method based on multi-dimensional element coupling, comprising: collecting the real-time and historical risk hidden danger data of associated rainfall station, water level station, flow station, soil moisture station in dangerous area;Combining flood data to construct HEC-RAS two-dimensional grid rainfall model;Determine the flood inundation range by rainfall model simulation, and determine the flood control capacity of specified object, determine mountain torrent disaster early warning rainfall;Introduce river engineering facility hidden danger, early soil moisture content, regional geological data and the multi-dimensional mountain torrent influencing factor in current hydrological state of river channel and construct corresponding data model;Based on a group of dynamic early warning rainfall and coupling associated multi-dimensional mountain torrent influencing factor data model, used to correct initial early warning rainfall threshold, to form dynamic perception mountain torrent disaster early warning index.The application can form dynamic situation perception early warning rainfall index, realize the transition from static risk to dynamic risk.

The invention discloses a drainage basin sediment production forecasting method and system considering a rainfall accumulation effect and a medium. The method comprises the steps of data acquisition and collection; the early-stage influence rainfall is calculated, and the index attenuation type early-stage rainfall index APIt is calculated; constructing a threshold set, determining a short-window rainfall high threshold P3, high and low threshold P3, low, and determining a long-window rainfall high threshold PL, high and low threshold PL, low; representing a sand supply state; an event type judgment rule; event type judgment; sand reduction and recovery of continuous events; performing cold start treatment; calibrating parameters; according to the method, a multi-scale early-stage influence rainfall index, sand supply state characterization and a continuous event sand reduction factor are introduced, a'sand supply-sand transport capacity 'mismatch mechanism is systematically described, and the prediction stability and interpretability under different hydrological years and multi-event sequence conditions are improved.

The present application belongs to the technical field of disaster prevention and mitigation and hydrological information intelligent processing, and particularly relates to a method and system for mountain flood warning of a lack of data basin based on few-shot learning. The method comprises: collecting rainfall events of a source basin and a target basin respectively, and constructing a corresponding event scale rainfall sequence for each rainfall event; then extracting event level rainfall features from the sequence, and combining the event level rainfall features with a previous rainfall index to form an event level feature vector; subsequently, taking the historical mountain flood event labels of the source basin as the basis, the feature vector and the corresponding label of the source basin form a support set, and the feature vector of the target basin forms a query set; under the premise that the mountain flood warning model is not trained and parameter adjusted by using the target basin samples, the support set and the query set are jointly input into the model according to a preset rule, and the prediction results of each rainfall event of the target basin are output; when the prediction result is determined as a mountain flood event, mountain flood warning information is issued. The present application realizes cross-basin migration of mountain flood identification without retraining the model.

The invention discloses a research area seasonal rainfall prediction method and system, and relates to the technical field of rainfall prediction. Comprising the following steps: taking monthly average reanalysis data of historical ERA5 data of a research area as a historical forecasting factor set; dividing the research area according to longitude lines and latitude lines, determining a seasonal rainfall index according to the rainfall sum of each radial grid point and each latitudinal grid point at the historical moment, and decomposing the seasonal rainfall index into a plurality of physical components; determining a causal relationship between the plurality of physical components and a historical forecast factor set, and determining a key influence area of each historical forecast factor influencing each physical component; constructing a historical rainfall forecast sample set according to the key influence region, and training the wavelet neural network to obtain a regional seasonal rainfall forecast model; and inputting the real-time forecasting factor data into the regional seasonal rainfall forecasting model to obtain the seasonal rainfall forecasting intensity of the research region. The method can effectively improve the prediction accuracy of seasonal rainfall.

The invention relates to the technical field of rainfall prediction, and particularly discloses a method for predicting abnormal rainfall in the main flood season of Beijing, Tianjin and Hebei in summer, which comprises the following steps: constructing rainfall circulation indexes of the main flood season from the next half of July to the first half of August in the Beijing, Tianjin and Hebei region; calculating to obtain a rainfall circulation index value of a prediction period based on latitudinal wind field prediction data output by a mainstream numerical mode at home and abroad, and performing rainfall quantitative prediction through the rainfall circulation index value of the prediction period; the method comprises the following steps: establishing a unary linear regression relationship with global sea surface temperatures in March to June of each year on the basis of main flood season rainfall indexes from the next half of July to the first half of August in the Beijing-Tianjin-Hebei region constructed on the basis of historical related rainfall data from the next half of July to the first half of August in the Beijing-Tianjin-Hebei region, and obtaining key sea areas associated with main flood season rainfall index abnormity in the current year; and performing rainfall qualitative prediction by taking the sea surface temperature of the key sea area as a key index.