111 results about "Flood forecasting" patented technology

The invention relates to the technical field of flood forecasting, in particular to a flood forecasting method, device and equipment based on field monitoring and real-time modelling, and the method comprises the steps: collecting a topographic data file and an earth surface data text of a target area to construct a sub-meter DEM digital elevation model; radar real-time base data, radar and ground rainfall historical observation data, brightness temperature data and geographical coordinate information of a target area are obtained to solve actual radar real-time base data, rain short-term forecast data and soil moisture data, and then the data are utilized to calculate a preset hydrological model to obtain a mountain torrent gully basin distributed hydrological model result; and establishing a hydrological-hydrodynamic coupling model according to the sub-meter DEM digital elevation model and the torrential flood gully basin distributed hydrological model so as to forecast the current flood state and the future flood state. Therefore, the problems that an existing mountainous area hydrological model cannot completely reflect the current state of a drainage basin, and the simulation error of a hydrodynamic model is large are solved.

The invention provides a power grid equipment large-range progressive real-time flood forecasting method based on a three-water-source Xinanjiang model, which abandons a basin homogenization hypothesis of a traditional lumped model, divides a target basin into subunits with spatial uniqueness and topological connectivity through a Thiessen polygon method, and accurately matches underlying surface spatial differentiation features; according to the method, the limitation of single parameter calibration is broken through, an NSGA-I I multi-objective optimization algorithm is adopted to be combined with historical flood data, a parameter optimization system containing multiple indexes such as a flood peak flow error and a peak present time error is constructed, a Pareto optimal solution set is generated, a parameter knowledge base is constructed in a classified mode, and the problem of poor parameter adaptability is solved; a geographically weighted regression method is used for fusing satellite remote sensing, ground rainfall station and hydrometric station data, a dynamic correction mechanism is established, model input is updated every 30 minutes, and the rainfall runoff calculation temporal-spatial resolution is remarkably improved; after subunit outlet flow riverway convergence calculation is completed based on a Muskingum method, power grid equipment space distribution and vulnerability threshold values are innovatively associated.

The invention discloses an intelligent early warning method and system for snow-melting flood. The method comprises the following steps: constructing digital twin integrating snow-melting confluence and hydrological hydrodynamic force; collecting multi-source monitoring information, and forming driving data through quality control and spatio-temporal interpolation; driving data are input into digital twinning, and model parameters and states are updated through data assimilation; simulating snow-melting runoff, water level and flow to obtain snow-melting flood probability prediction; the method comprises the following steps of: solving a river reservoir gate pump scheduling scheme by using a multi-objective evolutionary algorithm by taking reservoir outlet flow, gate opening and pump station starting and stopping as decision variables, calculating a water level, flow and a submerging range in digital twinning, and selecting a target scheme according to an evaluation rule to generate a scheduling instruction and graded early warning information; monitoring data is collected during scheduling execution and compared with probability prediction, and when the deviation exceeds a threshold value, data assimilation and scheduling scheme updating are triggered. According to the invention, the snow-melting flood forecasting precision and flood control scheduling collaboration are improved.

The invention relates to a river channel confluence stabilization and efficient calculation method of a distributed hydrological model CASC2D, and the method comprises the steps: employing a semi-implicit local inertial wave method in a river channel unit to carry out the stable solving of a water flow evolution process, effectively inhibiting the numerical oscillation, and improving the model calculation efficiency; meanwhile, parallel computing is carried out on the double-layer nested grid structure of the model through OpenMP, a thread safety control and dynamic scheduling mechanism is matched, synchronous waiting and data competition are remarkably reduced, and the parallel utilization rate is increased. The method is particularly suitable for flood simulation of small and medium-sized watersheds in mountainous and hilly areas, the model calculation efficiency can be improved by 15 times while high-precision simulation is kept, and the risk of numerical instability under the short-time high-intensity rainfall condition is remarkably reduced. The method does not need to depend on a GPU or a distributed cluster platform, has high universality, good portability and engineering realizability, and is suitable for various application scenes such as flood forecasting, risk analysis and real-time scheduling.

The invention discloses an urban rainstorm waterlogging process simulation method based on deep learning, and relates to the technical field of flood disasters. Comprising the following steps: constructing an urban inland inundation rainfall-water depth data set; training a ConvLSTM deep learning model by using the rainfall-water depth data set, and constructing a flood forecasting system based on the deep learning model; and constructing a training set and a verification set based on simulation results under different rainfall scenes, carrying out comprehensive evaluation on the performance of the ConvLSTM model, and carrying out verification in an actual scene. The coupling model provided by the invention not only improves the accuracy and speed of urban flood forecasting, but also enhances the adaptability to extreme weather events, and the future research focuses on further optimization of the model structure.

The invention provides a multi-mode coupling flood forecasting method and system based on a gated cross attention mechanism, and relates to the technical field of flood forecasting. The method comprises the following steps: acquiring high-precision rainfall monitoring information, numerical forecast rainfall information and hydrological station session flood excerpt information of a predetermined drainage basin; constructing training, verification and test samples; a spatial-temporal feature coding module based on a convolutional neural network and Transform is constructed, and rainfall information is fully mined; a time sequence feature coding module based on a time sequence convolutional network is constructed, and runoff information is fully mined; constructing a multi-modal feature fusion module based on a gated cross attention mechanism, and fusing different modal features mined by the time sequence and spatial-temporal feature coding modules; and constructing a flood prediction module based on a self-attention mechanism to perform flood prediction. According to the invention, the accuracy and reliability of flood forecasting are improved based on the multi-modal feature fusion technology.

The invention provides a watershed flood early warning system and method based on hydrological model and mobile application coupling, and belongs to the field of watershed flood early warning. In the system, a communication and data transmission module collects data for hydrological model simulation in a watershed area; the model simulation module simulates hydrological data of rainfall runoff in a watershed area in real time by using a hydrological model based on the collected data, and transmits a simulation result to the flood risk forecasting module through the communication and data transmission module; the flood risk forecasting module generates a flood risk map based on the received simulation result and transmits the flood risk map to the communication and data transmission module; and the mobile application display module receives real-time early warning information generated by the communication and data transmission module based on the flood risk map, and displays the real-time early warning information to a user. The watershed flood is simulated in real time through the hydrological model, the flood forecasting and early warning information is sent to people in a watershed flood submerging range through the mobile application, and the accuracy and timeliness of watershed flood early warning can be improved.

The invention discloses a flood forecast evaluation method adapted to reservoir dispatching characteristics, which comprises the following steps: respectively driving the same hydrological model by using historical observation rainfall and forecast rainfall, quantifying the error contribution of the hydrological model and rainfall forecast through double-track difference, and constructing a sample evaluation weight in combination with a rainfall score; constructing a weighting condition error distribution model based on the weight, and generating a reservoir flow forecasting scene set containing physical cause characteristics; and inputting the scene set into a reservoir scheduling model containing flood control and benefit-making rules, and outputting a comprehensive scheduling risk index containing a downstream over-alarm risk and water abandoning loss through water balance and downstream evolution calculation. According to the method, the statistical weight is corrected through error attribution, the abstract forecast error is converted into specific engineering risk and economic loss, the hidden risk working condition which is difficult to find by a traditional statistical index is effectively identified, and a quantitative basis is provided for risk decision making of reservoir dispatching and targeted optimization of a forecast system.

The invention discloses a large-scale flood risk dynamic assessment method for a power grid equipment flood-related area, and aims to solve the problems of risk assessment result distortion under complex hydrology, insufficient pertinence of power grid equipment flood risk assessment, insufficient risk assessment dynamic updating capability and the like in a traditional flood risk assessment method. According to the method, basic data such as meteorology, hydrology, terrain, remote sensing images and power grid historical flood conditions are integrated, and flood-prone areas are analyzed and determined; the method comprises the following steps: aiming at a flood-related area of power grid equipment, constructing a district distributed flood forecasting model based on a three-water-source Xinanjiang model, taking a hydrological forecasting model result as input, constructing a refined one-two-dimensional coupling flood routing analysis model, and simulating a flood inundation risk evolution process of the power grid equipment in different scenes; and constructing a power grid equipment multi-index flood inundation risk grading system to clarify the risk grade, constructing a large-scale flood risk assessment model, and outputting a dynamic risk assessment result by associating real-time data.

The invention discloses a reservoir flood forecasting method based on physical constraint and space-time double-flow coupling. The reservoir flood forecasting method comprises the following steps: S1, acquiring multi-source hydrometeorological data; s2, decomposing the multi-source hydro meteorological data into a historical state sequence and a future driving sequence; s3, performing feature extraction on the historical state sequence data through the physical enhanced long-short term memory network of the historical inertial feature extraction branch to obtain historical inertial features, and performing feature extraction on a future driving sequence through the time domain convolutional network of the future forced feature extraction branch to obtain future forced features; s4, performing weighted fusion on the historical inertial features and the future forced features to generate fusion features; and S5, inputting the fused features into a decoder to obtain a predicted water level increment, and superposing the predicted water level increment to the current water level to obtain a predicted value. The prediction timeliness is improved, the prediction precision of the water recession stage is improved, and the physical consistency of the prediction result is enhanced.

The invention discloses a method for forecasting flood in a drainage basin influenced by strong human activities based on a hybrid model. The method comprises the following steps: firstly, constructing a distributed hydrological model combining sub-drainage basin division and a Muskingum method, and performing calibration; using a hydrological model simulation error and multi-step watershed average rainfall based on river channel propagation time as characteristics, and using a Bayesian optimized LSTM (Long Short Term Memory) to construct an error correction model; and finally coupling the two models to realize real-time forecasting. Verification of the Lanxi river basin shows that compared with a traditional model, the Nash efficiency coefficient of the mixed model is remarkably improved, the flood peak relative error is smaller than or equal to 8%, the peak current time difference is smaller than or equal to 2 hours, precision is stable within the 12-hour forecast period, a long series of continuous historical data is not needed to serve as support, and only data in the session flood period are needed; the method is suitable for hour-scale flood forecasting of the watershed lack of data and strong human activity, an efficient and reliable technical means is provided for flood control and disaster reduction, and the method has wide engineering application prospects.

The invention discloses a river and lake regulation and storage system flood routing intelligent forecasting method based on a dynamic storage and discharge relation, and the method comprises the steps: constructing a space-time dynamic association diagram structure which reflects the waterpower relation of a drainage basin, and mapping multi-source hydrological data to generate a model input set with a unified space-time scale; establishing an aggregation reservoir parameterized structure model which comprises a dynamic water level-reservoir capacity relation driven by inflow change characteristics and a composite water level-flow relation integrating a time lag effect and backwater jacking feedback; constructing a flood regulation calculation equation set, and according to the real-time delay state in the composite relationship, adopting an adaptive solution mechanism to switch among different algorithm channels so as to solve the equation set; and correcting the model state by using the real-time observation data and executing rolling prediction. The method effectively solves the problems that in a complex river and lake system, dynamic characteristics of the storage and discharge relation are difficult to depict, the non-in-phase evolution process is unstable to solve, and high-dimensional parameters are difficult to calibrate, and the continuity and physical consistency of flood forecasting are improved.

The invention relates to the technical field of hydrology and water conservancy, and particularly discloses an intelligent real-time watershed flood early warning method and system, and the method comprises the steps: watershed data acquisition, fusion forecasting model construction, multi-stage early warning threshold calculation, early warning generation and online evaluation. According to the scheme, the real-time residual error of the physical hydrological model is constructed, the complex mode that the residual error changes along with the real-time rainfall intensity and the drainage basin state is learned by adopting the residual error prediction model based on machine learning, and an interpretable and steady forecast baseline and high-precision error correction are provided through a forward coupling and feedback coupling mixed architecture; the overall robustness of flood forecasting is effectively improved; dynamic inversion is carried out by using a fusion forecasting model, flood risk differences under different soil conditions are identified, the matching degree of early warning signals and actual risks is greatly improved, a comprehensive risk value is calculated in real time on a unified grid division system, and a risk zoning map is refreshed in real time along with updating of rainfall forecasting, so that the real-time prediction of the flood risk is realized. And the practicability of early warning information and the pertinence of emergency response are effectively improved.

The invention discloses a multi-target flood forecasting method based on coupling of a Xinanjiang model and deep learning. The method comprises the steps of 1, extracting a target watershed digital water system and subunit boundaries, and calculating watershed area rainfall; 2, hydrological data of a target drainage basin are collected, a data set is constructed, and an hourly rainfall-evaporation sequence and a secondary flood data set of the target drainage basin are obtained; 3, calibrating parameters of the Xinanjiang three-water-source model by adopting an NSGA-II optimization algorithm, generating a Pareto optimal solution set, and selecting a robustness parameter combination; and 4, jointly inputting the physical state variable output by the optimized Xinanjiang model and the original hydrological and meteorological data into a PatchTST deep learning model, capturing the nonlinear relationship between the physical state and the flow through a Transform architecture, generating corrected flow, and finally outputting a high-precision forecasting result. The method has the characteristics of multi-target collaborative parameter optimization, consideration of precision and physical consistency, accurate conversion of drainage basin geographic information and end-to-end high-robustness design.

The invention discloses a flood forecasting method and system considering similarity and a multi-dimensional classification system, and the method comprises the steps: obtaining the historical rainfall and runoff observation data of a drainage basin, and building a historical sample library and a test period of a similarity flood forecasting model; analyzing the influence time lag of early rainfall and runoff on the forecast section runoff; constructing rainfall-runoff combined similarity indexes, establishing a multi-dimensional classification system, and dividing runoff change modes according to a three-dimensional classification system; a similarity flood forecasting model is constructed, the model is trained for different runoff change modes, and model optimal parameters and forecasting schemes of all the runoff change modes are obtained; and integrating various types of models, constructing a flood forecasting model for automatically identifying a runoff change mode, automatically matching rainfall-runoff similarity of optimal parameters of the model and a multi-dimensional classification system, and generating hourly runoff forecasting in the future 72 hours through rolling forecasting. According to the method, the flood forecasting precision and robustness under the complex hydrological condition are improved.

The invention provides a GNSS (Global Navigation Satellite System) water vapor chromatography and deep learning-based flood forecasting method. The method comprises the following steps: S1, collecting GNSS, meteorological, flood and geographic data; s2, the PWV is estimated through a PPP equation and a Saastamoinen model; s3, grid water vapor density is calculated based on the three-dimensional water vapor chromatography technology; s4, constructing a Transform model, and performing rainfall prediction by taking PWV, water vapor density and the like as input; and S5, constructing a CNN-LSTM model based on rainfall prediction data to carry out flood forecasting. According to the rainfall and flood forecasting method established by the invention, GNSS multi-source and multi-mode navigation positioning signals are effectively utilized, the rainfall and flood forecasting method is not influenced by cloud and rain, all-weather observation can be realized, the water vapor gathering and conveying process in a three-dimensional space before rainfall can be carefully captured, the rainfall inversion process is optimized, the flood forecasting precision is improved, and the rainfall and flood forecasting period is prolonged; and a new thought is provided for flood control early warning of the drainage basin.

The application discloses a self-adaptive flood forecasting method, a terminal and a storage medium. The method comprises the following steps: generating a high signal-to-noise ratio local hydrological real-time data stream through adaptive sampling and a double-channel differential quantization technology; receiving an upstream input data stream, performing flood evolution calculation based on a viscoelastic dynamics model and fusing a dynamically identified input delay to obtain an original prediction value; for model parameters, a confidence ellipse is constructed based on ridge regression, the residual of real-time data and the prediction value is compared through statistical projection, the measurement noise and the river physical drift are intelligently distinguished, and the parameters are updated smoothly; finally, the original prediction value is corrected by solving a convex optimization problem subject to safety constraints, and a physically credible final prediction value is output. The corresponding terminal comprises corresponding modules for realizing the above steps. The application realizes high-precision, self-adaptive and intrinsically safe flood forecasting.

The invention belongs to the technical field of hydrological models and flood forecasting, and discloses a time-varying outflow coefficient-based flood runoff production simulation method, which comprises the following steps of interflow existence judgment, hydrological communication mechanism modeling, time-varying outflow coefficient model construction and parameter calibration and verification. According to the method, the behavior of the interflow is accurately described, the inflection point of the flood recession section can be accurately simulated, the flood forecasting precision of the semi-arid and semi-humid region is remarkably improved, and reliable technical support is provided for flood control and disaster reduction.

The present application relates to the technical field of hydrological resources, and specifically includes an intelligent short-impending fusion flood forecasting method based on a rain measurement radar, which comprises: receiving rain measurement detection information, performing prediction effect feedback in combination with a short-impending flood monitoring task at an edge adaptive correction node, configuring flood identification features based on a hydrological fusion prediction model, determining a flood peak time combination and a flooding risk, and generating a graded short-impending flood warning signal. The technical problem of the initial configuration of the hydrological fusion prediction model not matching the rain measurement data characteristics and the prediction target, the prediction deviation being unable to be corrected in time, and the deviation accumulation affecting the prediction reliability is solved by using a general hydrological model deployment mode, the technical effect of adaptively deploying the hydrological fusion prediction model by binding the core detection information of the rain measurement radar and the flood prediction accuracy requirement, the cooperative mechanism of the edge adaptive correction node and the RS485 bus interface, effectively avoiding deviation accumulation, and improving the stability of runoff simulation is achieved.

The invention relates to the technical field of flood prediction, solves the problem in the prior art that calculation of the total flow of a downstream section deviates due to neglect of superposition influence of reservoir flood during evolution along a river channel, and particularly relates to a reservoir dam break flood prediction method. Comprising the following steps: obtaining an initial parameter set of dam body parameters, hydrological parameters, terrain parameters and breach characteristic parameters of the rigid dam, preprocessing the initial parameter set to obtain a standard parameter set, and calculating a breach development rate and a breach width ratio when the rigid dam is identified as an instantaneous dam break according to the standard parameter set, and the instantaneous dam break is divided into a plurality of dam break sub-types in combination with the residual dam height proportion. According to the method, accurate time sequence superposition of the dam break flood and the reservoir flood is achieved, the calculation accuracy of the total flow of the downstream section is improved, the calculation result of the total instantaneous flow and the maximum flow of the downstream target section at any moment better fits the actual incoming flow condition, and integrated accurate prediction of the dam break flood flow, the propagation time and the downstream water level is achieved.

The present application relates to the technical field of flood forecasting, and more particularly to a city flood rapid forecasting method based on multi-process generalization. The method comprises the following steps: dividing sub-catchment by obtaining basic geographic information such as river system, terrain and rainwater pipe network of the target area, analyzing city underlying surface characteristics to calculate runoff, and calculating surface and pipe network confluence process based on improved equiflux line method and generalized trunk pipe model; through the interaction of trunk pipe and series-parallel virtual reservoir model, the surface detention, overland flow and return pipe are realized; finally, the one-dimensional river confluence and the cellular automata two-dimensional surface inundation model are coupled to realize the rapid simulation and visualization forecasting of city flood process. The present application efficiently couples runoff, surface confluence, pipe network flow limitation, surface and pipe network interaction, one-dimensional river confluence and two-dimensional inundation process through multi-process generalization, greatly reduces the calculation complexity and improves the timeliness of city flood forecasting.

The application discloses a large-scale basin deep learning flood forecasting method based on runoff lag information and relates to the technical field of hydrological simulation and flood forecasting of machine learning, and comprises the following steps: collecting basin hydrological observation site information data, basin characteristic data, basin historical hydrological data and meteorological forecasting characteristic data; dividing a large-scale basin into a plurality of sub-basins; preprocessing data, extracting runoff lag information obtained by using a convolutional neural network model, and constructing a runoff lag information database; adopting a deep learning long short-term memory model to couple the runoff lag information database, obtaining a plurality of sub-basin runoff lag information models; obtaining optimal runoff lag information parameters of the plurality of sub-basins according to a model precision evaluation index, obtaining an optimal hydrological forecasting model, and realizing accurate flood forecasting of the large-scale basin. The application makes up for the deficiency of flood forecasting under the condition of lack of hydrological data of a large-scale basin and has a strong application prospect in areas with little or no data.

The application discloses a time-varying runoff coefficient calculation method and system reflecting urbanization underlying surface regulation and storage, and steps are as follows: S1, obtaining rainfall data of a city research area, drawing a column chart and a curve chart of a rainfall process, and extracting high-precision digital surface model data by using high spatial resolution satellite stereo image pair data; S2, performing grid processing on the high-precision digital surface model data, constructing a grid type, and performing land utilization division and extraction analysis; S3, calculating hydrological parameters including a basin water content parameter, vegetation interception, a filling and depression parameter and a infiltration parameter; S4, calculating building roof runoff, effective impervious area, non-effective impervious area, pervious area and water area based on the rainfall process, the land utilization division and the hydrological parameters; and S5, calculating a time-varying runoff coefficient. The application can accurately evaluate the availability of rainfall water resources, provide scientific guidance for urban rainwater collection and utilization, and provide a more reliable basis for urban flood forecasting and drainage system design.

The invention discloses a mountainous area extreme rainstorm flood forecasting method based on an MUL-U2-NET network, and the method comprises the following steps: obtaining and preprocessing multi-source data, including hydrological element observation data and an image data set of a mountainous area flood forecasting factor, the image data set at least comprises extreme precipitation distribution, soil water content distribution, geographic elevation distribution, land cover type distribution, hydraulic engineering type distribution and night light distribution data; the method comprises the following steps of: constructing an MUL-U2-NET network model fusing multi-source information, wherein the model comprises an input layer, a coding module, a residual error segmentation module and a decoding module; training the model by adopting an unequal weight loss function; and inputting real-time data into the trained model for flood forecasting. According to the method, through multi-modal data fusion and network structure optimization, the problems of insufficient utilization of underlying surface information and poor model generalization ability in mountain flood forecasting are solved, and the forecasting precision is improved.

The invention provides a complex watershed short-time flood response intelligent forecasting method based on satellite rainfall perception, and relates to the technical field of hydro-meteorological monitoring and disaster prevention and reduction. The method comprises the following steps: acquiring satellite rainfall data of a target drainage basin observed by a multi-channel satellite, and preprocessing the satellite rainfall data; inputting the preprocessed satellite precipitation data into a short-time precipitation prediction model, and outputting a short-time satellite precipitation prediction result; and inputting the short-time satellite precipitation prediction result into a semi-distributed hydrological model, simulating and generating a flood response process of a watershed scale, and outputting predicted flood process data. According to the method, the precision problem of short-time rainfall prediction of the complex drainage basin is solved, and the timeliness and stability of flood prediction are improved.

The invention relates to the technical field of flood forecasting, in particular to a flow-data-free area flood forecasting method and system combining a Xinanjiang model and an intelligent algorithm. Comprising the following steps: reading a configuration file and rainfall flood process data; s2, training a Xinanjiang model based on the data in the step S1, obtaining a point set sum between the forecast flow and the water level, establishing a Z-Q coordinate system, and constructing a Z-Q function model and a curve by an AR (P) model; investment and operation risks of installing and maintaining high-cost flow monitoring equipment in complex terrains are avoided, feasibility and accuracy of flood forecasting in areas without flow data are improved, a reliable basis is provided for flood control project dispatching and safety early warning, improvement of the overall flood control and disaster reduction capacity of China is promoted, and social stability and economic sustainable development are guaranteed.

The application discloses a kind of strong human activity influence basin flood forecasting method based on hybrid model, first, the distributed hydrological model of combination sub-basin division and masking method is constructed and is rated;Again, with the characteristics of hydrological model simulation error and multi-step length basin average precipitation based on river channel propagation time, error correction model is constructed using Bayes optimization LSTM;Finally, real-time prediction is realized by coupling two models.It is proved by Lanxi basin verification that the Nash efficiency coefficient of hybrid model is significantly improved compared with traditional model, the relative error of flood peak is less than or equal to 8%, the time difference of peak appearance is less than or equal to 2 hours, and the accuracy is stable within 12 hours forecast period.The application does not need long series of continuous uninterrupted historical data as support, only needs the data during the period of flood, and is suitable for hour scale flood forecasting in data-deficient strong human activity basin, provides efficient and reliable technical means for flood control and disaster reduction, and has wide engineering application prospect.

This invention discloses a flood forecasting method for each flood event considering deep learning coupled with similar inflows, belonging to the field of hydrological forecasting technology. The method acquires a set of target floods and historical floods to be forecasted. Based on the feature vectors of each historical flood in the target flood and historical flood sets, it calculates the mechanistic similarity weight between the target flood and historical floods. It calculates the number of effective samples based on the similarity weights, adaptively filters and calibrates the sample set, and uses a perturbation gating mechanism to eliminate periods of human interference, constructing a weighted objective function to calibrate the model parameters. Using a data-driven model configured with a differentiable time-shifted layer, it corrects the amplitude and aligns the peak time of the preliminary forecast sequence of the physical model. This invention solves the problems of traditional similarity matching neglecting physical mechanisms and deep learning's difficulty in correcting phase errors, thus improving the accuracy and robustness of flood forecasting.

This invention belongs to the field of hydrological forecasting technology, specifically relating to a flood forecasting method based on ensemble Kalman smoothing data assimilation. This method is based on a conceptual hydrological model and uses real-time flow observation information to dynamically correct the model's state variables. It is applicable to scenarios such as basin flood process simulation, real-time forecasting of reservoir inflow floods, and flood control scheduling. Specifically, based on the Xin'anjiang model, this method constructs a data assimilation framework coupling the Xin'anjiang model with ensemble Kalman smoothing. It uses real-time flow observations to dynamically backtrack and correct key state variables of the model, and combines a bias-corrected Gaussian error model to correct state disturbance deviations constrained by physical boundaries, thereby improving the accuracy of flood process simulation and real-time forecasting. This method can also be optimized by combining different state update methods, ensemble size, backtracking steps, and observation weight settings to form an optimal data assimilation scheme suitable for real-time flood forecasting.

The application relates to the technical field of water conservancy management, in particular to a water conservancy informatization management method and management system based on digital twinning technology. The method comprises the following steps: a digital twinning model covering a target region is constructed; through an Internet of Things sensing network and remote sensing and other means, multi-source data is collected in real time and is synchronized with the model; the multi-source data is used to calculate the regional flood storage capacity composed of soil water storage capacity, river channel storage capacity and surface water body storage capacity; flood evolution simulation deduction is carried out based on regional weather forecasts and the flood storage capacity; and multi-level early warnings are generated and issued according to forward-looking indexes such as predicted flood storage capacity breakthrough time and inundation range. The application solves the problems of the prior art, such as dependence on static capacity evaluation and early warning reaction lag, quantifies the real flood storage capacity of the region dynamically and integrally, significantly improves the accuracy of flood forecasting and the advance amount of early warning, and provides strong scientific support for accurate flood control decision-making.