175 results about "Flood warning" patented technology

The invention relates to a hydropower station safety comprehensive management system based on a digital twinborn scene, which designs a hydropower station data resource coding system, establishes a hydropower engineering digital twinborn base, integrates all related geospatial data, basic data, monitoring data and business management data of a hydropower station, and constructs the hydropower station digital twinborn scene. Predicting displacement and deformation conditions of a building structure of the hydropower station and a change trend of important facility and equipment state indexes of the hydropower station, and predicting and pre-judging safety risks therein; the reservoir flow of the hydropower station, the upstream and downstream river section water level, the flow change and the power generation output and the power generation capacity of the hydropower station are forecasted or predicted, corresponding plans are provided for flood early warning or meteorological disaster early warning or water level over-limit early warning, and key links of the plans are simulated in combination with a simulation engine; and performing simulation calculation on different power generation and reservoir scheduling strategies to obtain an optimal scheduling scheme. According to the invention, the intelligent management of hydropower station engineering safety, scheduling safety and power generation safety is realized.

The invention provides a flood monitoring and early warning method and system based on multi-source remote sensing satellite data fusion. The method comprises the following steps: collecting various types of remote sensing satellite data in a flood area; preprocessing the collected remote sensing satellite data; integrating different scale features of the preprocessed remote sensing satellite data to obtain feature fusion; popularizing feature fusion based on a revisit channel attention module from a same-layer scene to a cross-layer scene; flood recognition is carried out by using the fused remote sensing image, and the boundary and range of a flood area are extracted; according to the identified flood area and the historical flood data, flood early warning is carried out in combination with meteorological and hydrological information, and an early warning result is obtained; and comprehensively analyzing the flood cause, the flood process and the flood influence of the flood event. By integrating multi-source satellite data, the invention aims to improve the accuracy of flood large-range detection and strengthen the prediction capability of flood trend, thereby ensuring the accuracy and reliability of flood space-time early warning.

The invention discloses a real-time and efficient basin flood inundation simulation method and system, and relates to the technical field of flood simulation. Multi-source data such as hydrology, remote sensing, weather and terrain are collected in real time, and cleaning, interpolation processing and format standardization are carried out; a data fusion technology and a dynamic weight distribution algorithm are utilized, the system integrates multi-source data to generate a high-precision data set, and the weight of each data source is dynamically adjusted according to the real-time performance, the precision and the space coverage degree of the data; factors such as gradient, elevation and land utilization types are considered, correlation coefficients are quantified, the inundation range and water depth distribution in the drainage basin are estimated, and visual display and early warning release are carried out through the GIS technology. In addition, the system assists in decision making, submerging risk index calculation, emergency evacuation path planning and resource scheduling scheme generation; the real-time performance and accuracy of flood simulation are effectively improved, the flood early warning and emergency response capabilities are enhanced, and powerful support is provided for decision makers.

The invention belongs to the technical field of meteorological disaster prediction, and discloses a flood early warning method based on rainfall multilayer perception. Comprising the steps of collecting meteorological parameter data, terrain parameter data, hydrological soil data and human activity data; preprocessing the collected meteorological parameter data, topographic parameter data, hydrological soil data and artificial activity data to obtain a meteorological feature data set, a topographic feature data set, a hydrological soil feature data set and an artificial activity feature data set; fusing the meteorological feature data set, the topographic feature data set, the hydrological soil feature data set and the human activity feature data set to obtain a comprehensive feature data set; training according to the comprehensive feature data set to obtain a flood risk prediction model, and predicting through the flood risk prediction model to obtain a flood risk index; the predicted flood risk index is compared with a preset flood risk index threshold value, and whether flood early warning needs to be carried out or not is judged; and a worker is helped to accurately make flood control measures.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention relates to the technical field of outdoor water source exploration, in particular to a reservoir water conservancy monitoring system based on a multi-sensor fusion technology. The method comprises the following steps: firstly, collecting and processing reservoir water conservancy survey data of a reservoir through various sensors, wherein the reservoir water conservancy survey data comprises water source parameter survey data and reservoir structure survey data; hydrological changes of reservoir water conservancy and stress changes, pressure changes and vibration strain changes of a reservoir structure are comprehensively analyzed from the three angles of statistical characteristics, time sequence characteristics and spatial characteristics, and flood situation evaluation indexes of different dimensions are obtained; and finally outputting flood early warning information according to an analysis result. According to the invention, through multi-point survey of hydrological and structural changes of the reservoir structure, the safety monitoring precision of water conservancy of the reservoir in the flood season is improved, and real-time and intelligent risk early warning and decision support are provided for safety survey management of an open-air water source.

The invention relates to the technical field of flood early warning, and discloses a drainage basin flood early warning method based on regional data, which comprises the following steps: acquiring rainfall duration of a set region, and calculating a riverbed convergence rate of the set region as an auxiliary function to be fused into a preset BP neural network so as to construct a river reach runoff risk early warning model and output a river reach runoff risk level; the sediment inflow mode and the sediment content of a set area are obtained, the potential sediment risk of the river reach is determined, and the water and sediment risk of the river reach is further determined in combination with the rainfall and the sediment content; and taking actually monitored rainfall and forecast rainfall as driving data, obtaining a predicted water level through the distributed hydrological model, and issuing early warning in the set area in combination with the river reach water and sediment risk. According to the method, the river reach runoff risk early warning model is constructed, the sediment risk level identification mode is determined in combination with the sediment inflow mode, the sediment content and the actual rainfall, the water and sediment disaster risk assessment early warning method considering the sediment effect is provided, and the effectiveness and reliability of flood disaster prevention and control are improved.

The invention relates to an intelligent drainage basin management method, device and equipment based on digital twinning and a medium of the intelligent drainage basin management method and device based on digital twinning. The method comprises the steps that core terrain attributes are extracted through terrain feature decoupling and coding, and a disaster response function of an optimal source drainage basin is migrated based on terrain and rainfall similarity; a hydrodynamic model and a migration function are combined to generate a physically constrained synthetic disaster situation data set, a two-channel neural network model fusing topographic features and rainfall dynamics is constructed, and model parameters are dynamically calibrated through Bayesian continuous learning by using real-time monitoring data of a target drainage basin. Finally, a digital twinborn system with flood routing prediction capability is formed, the problem of rapid construction of a flood prediction model under the condition of no historical data is solved, and the timeliness and accuracy of early warning of sudden flood in small and medium-sized watersheds are remarkably improved.

The invention discloses a flood early warning decision analysis system and method based on artificial intelligence, and belongs to the technical field of flood early warning. Constructing a monitoring area database, wherein the monitoring area database comprises monitoring area rainfall data and area flood control data; based on the monitored area database, calculating the total precipitation of the monitored area within a preset time; calculating a risk value of flood occurrence of the monitored area; constructing a regional flood model; updating model parameters, and optimizing the regional flood model; when the deviation sum of the predicted value of the regional water level and the actual value of the regional water level reaches the minimum, finishing regional flood model optimization; when the risk value of the flood occurrence of the monitored area is greater than or equal to a risk threshold value, predicting the water level of each area of the monitored area through an area flood model; early warning lists are generated, and the early warning lists of all areas in the monitoring area are gathered and fed back to a monitoring area administrator; therefore, regional and refined early warning of flood disasters is realized.

The invention discloses a river flood early warning device, which relates to the technical field of flood prevention early warning equipment and comprises an L-shaped mounting plate, a waterproof box, a horizontal mounting plate, a mounting frame, a guardrail, a vertical mounting plate, a warning lamp, a flow velocity monitoring fan, a water level monitoring mechanism, a transmission mechanism and an alarm mechanism. The early warning device is driven by a second sliding mechanism to work, the two flow velocity monitoring fans are driven by a first sliding mechanism to slide, the vertical mounting plate is driven by the second sliding mechanism to slide, the second sliding mechanism is driven by a transmission mechanism to work, and the multiple protection fences are driven by a third sliding mechanism to slide. The water level monitoring mechanism can carry out monitoring and early warning on the river water level in real time, the alarming mechanism can effectively carry out monitoring and alarming on the water flow speed, the guardrail can slide in a reciprocating mode under driving of the third sliding mechanism, and when the water level rises, the guardrail is lifted up, and the protection effect can be effectively achieved.

The invention discloses a sky-ground integrated remote sensing emergency monitoring method for post-flood road safety, and relates to the technical field of remote sensing monitoring. The method comprises the following steps that an unmanned aerial vehicle collects highway pavement video image data after flood, and a terrain flood model is constructed; rainfall early warning sites are marked in the terrain flood model, and a corresponding flood early warning requirement is marked for each rainfall early warning site; and obtaining a road model in the terrain flood model, and generating an obstacle model on the road model according to the real-time data collected by the unmanned aerial vehicle. According to the invention, the terrain flood model is constructed for the video image collected by the unmanned aerial vehicle, the rainfall threshold value of each area is marked in the terrain flood model, and when the geological disaster occurs, the actual condition of the obstacle model is displayed on the terrain flood model, so that the timeliness and accuracy of flood early warning are improved, and the occurrence of the geological disaster is displayed in time. Obstacles on the road are visually displayed, and rescue after disasters can be conveniently and rapidly carried out.

The invention discloses a flood disaster early warning decision optimization method and device, a medium and a product, and relates to the field of environmental disaster management and prediction.The method comprises the steps that a decision feature matrix is determined according to a multi-dimensional decision situation; determining certainty and other values according to the decision feature matrix, and dynamically calibrating psychological parameters; a Gini index is improved according to the decision feature matrix and the weight of the flood event occurrence probability, and a split decision tree model is constructed based on the improved Gini index; determining a key split point on the flood event occurrence probability according to the trained split decision tree model; determining an adaptive decision threshold according to the flood event occurrence probability and the key split point; determining a framework effect compensation factor; adjusting an adaptive decision threshold by using the framework effect compensation factor; and determining a flood disaster early warning decision according to the adjusted threshold value. According to the invention, the accuracy of flood early warning decision and the system robustness can be improved.

The present invention proposes a flood monitoring and early warning method and system based on the fusion of multi-source remote sensing satellite data. The present invention collects various types of remote sensing satellite data of flood areas; pre-processes the collected remote sensing satellite data; integrates the pre-processed multiple remote sensing satellite data with features of different scales to obtain feature fusion; generalizes the feature fusion based on the revisited channel attention module from the same-layer scene to the cross-layer scene; utilizes the fused remote sensing images to identify floods and extract the boundaries and scope of the flood area; conducts flood warnings based on the identified flood areas and historical flood data, combined with meteorological and hydrological information, to obtain warning results; and conducts a comprehensive analysis of the causes, processes, and impacts of flood events. By integrating multi-source satellite data, the present invention aims to improve the accuracy of large-scale flood detection and enhance the ability to predict flood trends, thereby ensuring the accuracy and reliability of spatiotemporal flood warnings.

The invention relates to the field of hydrological forecasting, and discloses a distributed hydrological model construction method, system and equipment based on feature units, and the method comprises the steps: building a session flood historical data set according to the collected topographic data, soil data, hydrological data and meteorological data, dividing the topographic data in the session flood historical data set into feature units, the other soil data, hydrological data and meteorological data are matched to each feature unit, and a spatial topological relation is constructed based on the feature units; constructing a distributed hydrological model based on the spatial topological relation to calibrate runoff production model parameters, and calibrating confluence model parameters according to a runoff production model parameter calibration result; and according to a parameter calibration result, various monitoring and forecasting information and different levels of flood early warning release standards, manufacturing and releasing flood early warning information. According to the method, the distributed hydrological model is constructed based on the feature units, and real-time flood operation forecasting can be carried out.

The present invention provides a flood warning detection method and system based on a GCN graph convolutional neural network, the method comprising: S1, deploying multiple data acquisition devices in a target area; the data acquisition devices include online monitoring devices for real-time acquisition of original water flow data at the monitoring location; S2, preprocessing the original water flow data and processing outliers to obtain processed water flow data; S3, converting the processed water flow data into a vector form suitable for input into a neural network, and constructing a graph structure of the water flow data; S4, constructing a graph convolutional neural network architecture, training and optimizing the network; using an adaptive algorithm to dynamically adjust model parameters, and an output layer generates flood warning results; S5, training historical water flow data and real-time water flow data, and constructing a flood warning detection model; S6, using the flood warning detection model to perform flood warning prediction, and outputting the obtained real-time flood warning information to a user interface; the method can provide more accurate and reliable prediction results for flood warning.

The invention discloses a flood classification method and system based on dynamic time warping and multi-cluster coupling. The method comprises the following steps: firstly, collecting and processing flood flow time sequence data to obtain a data set composed of standardized floods; secondly, calculating the similarity between every two of the K floods by adopting a dynamic time warping method to form a similar matrix; and flood classification is carried out based on DTW coupling hierarchical clustering and K-medoids multi-clustering. The system comprises a data acquisition module, a data preprocessing module, a comparison module and a classification output module. According to the method, hierarchical clustering is optimized again, and a multi-clustering method is combined, so that the reasonability of flood classification is improved, and the accuracy of flood classification is improved. The method can be applied to the fields of flood early warning, flood control dispatching and the like, and has important practical value and social significance.

The present invention discloses a basin flood risk assessment method and system based on a particle recognition algorithm, and belongs to the field of flood warning technology. Although the existing scheme can measure the real-time flow of the river, it does not involve how to deduce and evaluate the flood inundation situation, resulting in the inability to quickly know the flood inundation situation around the river bank. The basin flood risk assessment method based on a particle recognition algorithm of the present invention, by constructing a basin spatial discrete model, a flow velocity calculation model, a flow simulation model, a two-dimensional hydrodynamic model, and a flood risk assessment model, can not only calculate the cross-sectional flow of the basin, but also deduce and evaluate the flood inundation situation, so that the flood inundation risk and inundation data around the water area can be quickly known, so as to meet the needs of basin flood risk analysis, improve the accuracy of flood disaster warning, and provide more data supplement and management basis for basin flood control.

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.

A method for evaluating terrain uncertainty in flood warning and forecasting is provided. The method includes: S1, acquiring three types of digital elevation model (DEM) data from a shuttle radar topography mission (SRTM), an advanced spaceborne thermal emission and reflection radiometer (ASTER), and an advanced land observing satellite (ALOS); and preprocessing the three types of DEM data; S2, optimizing urban terrain characteristics; S3, constructing a multidimensional parameter space by using Latin hypercube sampling (LHS); S4, calculating flood hydrodynamics numerical value based on multidimensional sample points; and S5, constructing a global sensitivity analysis method frame suitable for urban terrain characteristics-related factors, where a Sobol quantitative method is used in the global sensitivity analysis method frame, and the Sobol quantitative method is used to evaluate uncertainties and sensitivity characteristics of multiple factors of terrain data based on a variance decomposition theory.

The invention provides an urban subway flood early warning system based on a knowledge domain language model, and the system is characterized in that the system comprises a data collection module which is used for collecting the real-time data of a subway pipe network and the surrounding environment; the preprocessing module is used for preprocessing the real-time data to obtain preprocessed data; the prediction module is used for generating a subway flood prediction result according to the preprocessed data; the early warning judgment module is used for judging whether an early warning signal is generated or not according to the prediction result and the real-time water level in the real-time data, if yes, the early warning signal of the corresponding grade is generated, and if not, no early warning exists; the early warning text generation module comprises a knowledge domain language model and is used for generating flood early warning information corresponding to different objects according to the real-time data, the early warning signal and the prediction result; and the information sending module is used for sending the flood early warning information to a corresponding object. In a word, the method can automatically perform early warning on subway flood and generate flood early warning information for each crowd.

A system for monitoring flooding can include a plurality of flood monitoring stations and at least one flood warning station in communication with each flood monitoring station. Each flood monitoring station can include at least one water level sensor and at least one rainfall sensor. Each flood monitoring station can determine a water level at a periodic rate and compare the water level to a threshold, determine a rainfall accumulation at a periodic rate and compare the rainfall accumulation to a threshold, assign a threat level to the flood monitoring station, and selectively communicate a data packet to the flood warning station.

The invention discloses a flood priority scheduling intelligent evaluation method for multi-modal flood early warning. The method comprises the following steps: step 1, rainfall, water level and flow velocity data are collected and preprocessed to generate standardized multi-modal input; 2, constructing a river network graph structure, and extracting node and edge features to generate graph structure input data; step 3, inputting the graph structure into an EvolveGCN evolutionary graph convolutional network, embedding scheduling path accessibility, resource bottleneck and execution constraint features, and outputting a state propagation feature set; step 4, generating a node state prediction vector according to the state propagation characteristics; 5, calculating three types of risk scores, and applying nonlinear constraints to construct a priority score set; step 6, generating a scheduling action sequence and collecting feedback data; and step 7, updating the convolution structure by using the feedback updating score and the scheduling sequence, and outputting a final instruction. The flood scheduling intelligent prediction closed loop is realized, and the response efficiency and the scheduling feasibility are improved.

The invention relates to a flood early warning and valve linkage control system based on the Internet of Things. A river topology construction module dynamically generates a topology network representing a river structure by fusing geographic information and real-time sensing data; the early warning weight calculation module generates a quantitative risk index in combination with the water level overrun degree and the historical flood probability; the water flow time matrix generation module calculates a flood propagation time sequence based on a hydraulic evolution model; the flood early warning control module synchronously outputs an early warning report and a valve control instruction through a space-time risk coupling mechanism to realize intelligent linkage of early warning and control; the topology model correction module dynamically optimizes the river channel parameters and the risk model according to the valve execution feedback. According to the system, the problems of early warning response lag, model prediction misalignment and extensive valve control in traditional flood control are solved, early warning and control collaborative decision is driven through data closed loop of the Internet of Things, and the accuracy and reliability of flood control response are improved.

A flood warning apparatus may include: an extendable body that extends above a foundation, where the extendable body is configured to move within a range bounded by a minimum height and a maximum height based on a level of water in a waterway that covers the foundation; a platform disposed atop the extendable body, wherein the platform is buoyant; and. a floatation object having a notification mechanism, wherein the floatation object is configured to be retained by a retention feature of the platform when the position of the extendable body is less than the maximum height, where the floatation object is further configured to be released from the platform by the retention feature when the level of water in the waterway exceeds a threshold value, where the floatation object is buoyant, and where the notification mechanism activates when the floatation object is released from the platform by the retention feature.

This invention relates to an airport rainfall and flood early warning method, electronic device, and medium based on a large language model, for use in sponge airports. The early warning method includes: based on a rainstorm and flood management model, using a large language model for data augmentation to generate a multi-factor flood early warning dataset containing regionally specific factor data and airport-specific factor data; based on a retrieval-enhanced generation mechanism, vectorizing and calculating feature bias on the current input data, retrieving the most relevant historical case context information from the multi-factor flood early warning dataset; adaptively fusing the multi-factor features and the most relevant historical case context information, extracting key information through multi-head self-attention and attention pooling, and outputting the flood risk level and early warning result. This invention can significantly improve the accuracy, real-time performance, and interpretability of flood risk assessment, providing more reliable, traceable, and intelligent flood early warning and decision support for sponge airports.

The application relates to the technical field of agricultural early warning, in particular to a multi-stage linkage agricultural flood early warning information automatic broadcasting system, and the following scheme is provided: a sensing terminal collects farmland images, combines environmental data, analyzes waterlogging features by using a graph neural network, and thus precise flood disaster risk assessment is realized. After disaster identification, the system dynamically adjusts the broadcasting range and the communication protocol according to the risk level, so as to ensure that the early warning information is rapidly transmitted to the receiving terminal. In addition, an iterative operation mechanism is adopted, a cluster head terminal is selected according to the consistency of the waterlogging features, information redundancy transmission is avoided, and network resource use is optimized. The application improves the real-time performance, accuracy and resource utilization efficiency of the flood disaster early warning system, and has obvious innovation and application value.

This invention relates to a method and system for real-time flood warning in watersheds based on multi-source data fusion, belonging to the field of flood warning technology. The method includes: acquiring multiple multi-source data from a target watershed and preprocessing the data; fusing the preprocessed multi-source data to obtain fused data; and inputting the fused data into a trained real-time flood warning model for processing to obtain flood warning results for the target watershed. The real-time flood warning model is trained based on a CNN-LSTM model. During the training process, a Bayesian optimizer is used to optimize the hyperparameters of the CNN-LSTM model so that the feature flattening layer in the real-time flood warning model flattens the input multi-dimensional features into a one-dimensional vector based on the optimized hyperparameters, thereby improving the accuracy and timeliness of flood warnings.

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 provides a method and device for issuing flood early warning in advance through upstream and downstream linkage, belongs to the field of flood early warning, and is used for solving the problem that flood early warning is difficult to accurately, timely and reliably carry out in related technologies. The water level of upstream and downstream monitoring points of a river basin and key features of flood influence factors are intelligently analyzed by utilizing a self-research algorithm model, flood early warning scores are analyzed step by step and emphatically by utilizing the key features, the result of the flood early warning scores obtained by final analysis is ensured to be more accurate and reliable, flood early warning is issued in advance by utilizing the flood early warning scores, and the flood early warning efficiency is improved. The flood early warning can be timely, accurately and reliably issued in advance, so that the purpose of reducing and even avoiding economic loss and casualties caused by flood is achieved.

The invention provides a flood prediction method, device and equipment for a mountainous area hydropower station. The method comprises the steps of obtaining meteorological data in a drainage basin where the mountainous area hydropower station is located in real time; the meteorological data comprises rainfall data, flow data and water level data; respectively inputting the meteorological data into a plurality of pre-constructed flood prediction models, and determining a prediction result of each flood prediction model in the current period; wherein the flood prediction model comprises a time sequence model, a hydrological model and an LSTM and runoff coefficient model; determining a prediction error of each flood prediction model according to the prediction result and the water level data in the current period; according to the prediction error, determining a flood prediction model adopted in the next period and a corresponding prediction result; and issuing corresponding flood early warning according to the prediction result of the next period. According to the invention, through a multi-model parallel prediction and dynamic selection mechanism, the adaptation shortages of a single model under a complex water regimen are effectively made up, and the prediction error is further reduced.