135 results about "Weighted distance" patented technology

The invention discloses an unmanned aerial vehicle airport site selection method suitable for large-scale electric power line inspection, and the method comprises the steps: S1, carrying out the primary selection of a geographic space, constructing a geographic constraint evaluation model, carrying out the processing of elements and historical disaster data through discrete coding, generating a safe and suitable region through the multi-factor weighted stacking space analysis, and carrying out the calculation of an evaluation result; and a K-Means clustering algorithm based on Euclidean distance is combined to screen a primary selection region. S2, risk assessment preselection: constructing an entropy weight-TOPSIS risk assessment model, determining a risk factor weight by using an entropy evaluation method, calculating a scheme closeness degree through a TOPSIS algorithm, and screening a low-risk preselection region; and S3, constructing a binary relationship between a demand center set and a pre-selected site set by combining an electric power inspection target and taking an electric power inspection task target as a demand node, measuring weighted traffic cost through Euclidean distance, designing a double-layer nested 0-1 decision variable, and realizing optimization of an airport deployment area by taking minimization of a total weighted distance as a target function.

The invention relates to the technical field of data processing, in particular to an abnormal mode data processing system driven by power marketing big data, which comprises a distributed collaborative acquisition module for constructing a space-time alignment three-dimensional data stream, a multi-modal feature reconstruction module for separating periodic noise and quantizing environmental interference, and a data processing module for processing abnormal mode data. The resistance feature decoupling module generates a purification feature vector set and a noise confidence index through orthogonal projection, and the dynamic algorithm adaptation module dynamically schedules an isolated forest algorithm, a weighted distance measurement algorithm and a sparse self-encoding clustering algorithm according to the noise confidence index. The behavior chain verification module establishes a combined physical rule verification mechanism of an environment temperature threshold value, a load deviation degree and an equipment state, and the closed-loop strategy engine module adaptively adjusts a feature decoupling loss function weight according to a decision boundary offset, so that the accuracy and the environmental adaptability of real electricity consumption abnormity identification in a complex noise environment are effectively improved.

The invention discloses a civil aviation passenger demand prediction method and system based on artificial intelligence. The method comprises the steps of data acquisition, abnormal demand elimination, civil aviation demand side sample supplementation, civil aviation passenger demand prediction model establishment and civil aviation passenger demand prediction. The invention belongs to the field of civil aviation demand prediction, and particularly relates to a civil aviation passenger demand prediction method and system based on artificial intelligence. According to the scheme, a time sequence weighted distance is introduced, and it is guaranteed that samples with off-season and low demand characteristics are not mistakenly removed; on the basis of route similarity weighting, trusted neighbors are generated, and rare low-demand samples are supplemented in a targeted manner; feature importance scores are introduced, feature sensitivity scores of civil aviation samples are calculated, and influences of feature-time points on demand prediction are evaluated from different angles; the adaptability to festival and holiday peaks and extreme weather scenes is guided through a punishment mechanism; based on the introduction of a holiday association weight enhancement scheduling strategy, the penalty weight growth rate is dynamically improved; and the reliability of civil aviation passenger demand prediction is improved.

The invention discloses a network security threat detection system based on artificial intelligence. The system comprises a network security data acquisition module, a network security data optimization module, a network security threat prediction model establishment module, a model optimization module and a network security threat intelligent detection module. The invention relates to the technical field of network security data processing, in particular to a network security threat detection system based on artificial intelligence, which innovatively introduces weighted distance measurement driven by field correlation, adaptive neighbor number selection and a distance deviation threshold control strategy. A K-nearest neighbor method is improved to realize data completion, and the accuracy of a network security threat detection result is improved; a double-layer long-short-term memory network and self-adaptive threat threshold calculation are provided, and high-precision threat prediction of network security is realized; a sine smooth attenuation inertia weight strategy and a sine modulation contraction factor strategy are introduced to improve a particle swarm optimization algorithm, and the prediction accuracy and stability of the model are improved.

The invention provides a photovoltaic module surface defect positioning method based on image processing, and the method comprises the steps: carrying out the preprocessing of an original image, obtaining a gray image, obtaining a second abnormal seed point through a multi-level abnormal seed point screening strategy, including global statistical judgment, gray deviation analysis and neighborhood aggregation optimization, and obtaining a second abnormal seed point; and taking the second abnormal seed point as a starting point, realizing accurate extraction of a real defect area of the photovoltaic module through a self-adaptive multi-threshold iterative expansion algorithm in combination with weighted distance measurement of gray, gradient and texture, and finally performing characteristic parameter analysis on the defect area to realize identification and classified output of real defects. The method has the advantages of being high in positioning precision, high in robustness and suitable for various defects, and is suitable for automatic detection of the surface defects of the photovoltaic module.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

The application discloses a kind of big data-based optimization analysis method and system of territorial space planning, it is related to big data and optimization analysis technical field of territorial space planning, including real-time data acquisition and dynamic preprocessing, dynamic regional division, multi-model integrated space-time prediction, multi-objective dynamic optimization planning, real-time feedback and adaptive adjustment.The big data-based optimization analysis method of territorial space planning provided in the application adopts space-time weighted fuzzy clustering method, divides the territorial space into several sub-regions according to the spatial coordinates and time attributes of data, constructs the weighted distance regular objective function between data points and regional center, and updates the membership and the position of regional center iteratively, realizes the self-adaptation and space-time smoothing of regional segmentation, effectively captures the dynamic change characteristics in region.

The invention discloses a multi-period time difference weighted ranging method based on obstacle perception, which relates to the technical field of intelligent perception and sensor fusion, and specifically comprises the steps of multi-period time difference measurement, obstacle attenuation coefficient modeling, weighted fusion calculation, dynamic error compensation and final distance output. According to the multi-period time difference weighted distance measurement method based on obstacle perception, through multi-period time difference measurement and weighted fusion, random errors are reduced, and the distance measurement precision is effectively improved; by introducing the number of obstacles as a key variable, the weight of a distance measurement formula is dynamically adjusted to adapt to a complex scene, and dynamic environment adaptation is realized; and through combination with an obstacle attenuation model, the influence of multi-path interference on a distance measurement result is effectively suppressed, and the anti-interference capability is effectively enhanced.

The invention discloses a hydroelectric equipment anomaly detection method based on physical mechanism guidance and sequential topological entropy fluctuation characteristics, and belongs to the technical field of hydroelectric equipment monitoring and fault diagnosis. The method comprises the steps that multi-source sensor data of hydroelectric equipment is collected and preprocessed; constructing a physical weighted distance function in combination with an equipment physical mechanism, and embedding time sequence data into a high-dimensional point cloud space; extracting persistent homology features through a sliding window, generating a persistent graph sequence and calculating topological feature indexes; a persistence graph entropy fluctuation index is provided, and anomaly detection is realized by quantifying time sequence fluctuation of topological entropy; and finally, visual output and an alarm mechanism are combined to assist diagnosis. According to the method, equipment physical characteristics and topological data analysis are fused, the problems that a traditional method is insufficient in nonlinear system modeling, insensitive to dynamic evolution and the like are solved, the early warning capacity and detection precision of early faults are improved, and the method is suitable for anomaly detection application of core equipment such as a water turbine and a generator.

The invention provides a traffic tunnel preventive monitoring and risk early warning system based on an unmanned aerial vehicle group, and relates to the technical field of traffic tunnel safety monitoring, and the method comprises the steps: laying reference mark points on the inner surface of a tunnel as monitoring nodes, collecting the crack width, surface temperature and deformation of each node through the unmanned aerial vehicle group, and calculating the crack width, surface temperature and deformation of each node; and constructing a multi-dimensional feature vector and calculating a space-time weighted distance in combination with the space coordinates and the timestamps. And adjusting a neighborhood search radius according to the tunnel curvature, identifying a risk region through a density clustering algorithm, and marking unclustered abnormal nodes as isolated risk points. And dividing risk levels based on comparison of the structure safety degree index and a dynamic threshold value, and registering a result with the BIM model to realize three-dimensional visualization. Trend early warning is triggered for an area with monotonically decreasing monitoring safety index for three continuous times, full-process automatic monitoring from data acquisition, intelligent analysis to risk early warning is realized, and the accuracy and timeliness of tunnel structure health monitoring are remarkably improved.

The present invention discloses a heat load prediction method and system for district heating, belonging to the field of data processing technology. The method includes: data collection, data preprocessing, district heating data optimization, construction of a heat load prediction model, and intelligent prediction. This solution combines random forest split gain and mutual information to obtain the global significance of features and select the initial centroid, fuse Euclidean distance and mutual information, calculate weighted distance and perform clustering, calculate association purity based on mutual information and information entropy, and filter and delete redundant features based on redundancy conditions; generate a bimodal base adjacency matrix through a gating mechanism, generate a hidden feature matrix and a dynamic propagation adjacency matrix based on diffusion graph convolution, fuse the two adjacency matrices to generate a gated adaptive adjacency matrix, extract spatial features based on diffusion graph convolution, and extract temporal features based on spatiotemporal embedding and multi-head attention mechanism, thereby reducing prediction bias and improving the heat load prediction speed while ensuring accuracy.

The present invention relates to a multi-source data fusion method, system, electronic device and storage medium. The fusion method comprises: aligning the timestamps of measurement data to be fused with existing valid fusion targets; performing weighted summation of the Mahalanobis distance of the position and the Euclidean distance of the velocity between the measurement data and the aligned valid fusion targets to obtain a weighted distance, and calculating an association threshold range and an association probability between the measurement data and the aligned valid fusion targets based on the weighted distance; judging whether the measurement data is associated and matched to any valid fusion target to generate a matching pair based on the association threshold range and the association probability; for each matching pair, updating a filtering algorithm in the valid fusion target using the measurement data; performing weighted calculation of the Mahalanobis distance of the position vector and the Euclidean distance of the velocity vector between the fusion target and the sensor measurement, and calculating the joint association probability between the fusion target and the sensor measurement based on the weighted distance, thereby improving the association matching accuracy.

The invention discloses a film and television content intelligent recommendation method and system based on artificial intelligence. The method comprises the steps of multi-source data collection, data optimization, group film and television content recommendation, personalized recommendation optimization sorting and film and television content intelligent recommendation. The invention relates to the technical field of data processing, in particular to an intelligent film and television content recommendation method and system based on artificial intelligence. According to the scheme, group recommendation and personalized optimization are innovatively combined, and dynamic balance between group representativeness and individual difference is achieved; a density correction mechanism, weighted distance calculation, a representative point merging mechanism and a fuzzy weighted distribution strategy are innovatively proposed to improve the clustering algorithm, so that the clustering accuracy is improved, and the group recommendation precision and the user satisfaction are improved; and an adaptive reference individual selection strategy and an iteration-based global exploration weight improvement optimization algorithm are adopted, so that the accuracy of a model output result is remarkably improved, and the stability of personalized film and television content recommendation optimization sorting is realized.

The invention provides an automatic test method and system for an intelligent power distribution automation terminal, and relates to the technical field of automatic test, and the method comprises the steps: obtaining the model information and environment information of a to-be-tested terminal; generating test data of the to-be-tested terminal in combination with the model information, the environment information and the Lyapunov index; inputting the test data into the to-be-tested terminal, and outputting operation data; analyzing the operation data by using a dynamic threshold value based on the potential energy field, and outputting abnormal data; fusing and classifying the abnormal data based on a pre-training model and the coupling weighting distance, and determining a fault type; and performing early warning according to the fault type. According to the analysis method based on the Lyapunov index and the dynamic threshold value, the test can comprehensively cover various functions and limit states of the equipment, so that potential anomalies and faults can be effectively found.

The invention discloses a disparity map repairing method and system based on iterative filling, and the method comprises the steps: obtaining disparity map data of a target scene and possible missing values in a disparity map through initializing the disparity map; scanning each pixel point in the disparity map, and filling a hole region of the disparity map by adopting a weighted average value; further filling and repairing a cavity region in the filled disparity map by using an edge perception filling method; and performing multiple iterations on the results subjected to the weighted distance filling and the edge perception filling, further correcting a missing value region in the disparity map by using the weighted distance filling and the edge perception filling after each iteration, and finally repairing the disparity map. According to the method, the processing precision of the disparity map is improved, the holes can be accurately filled in a complex scene, and the method has a wide application prospect.

The invention discloses a line sequence automatic detection algorithm based on machine vision and an implementation method thereof, and belongs to the technical field of machine vision and image processing. Aiming at the problems of low efficiency and poor accuracy of traditional manual detection of a multi-core cable, the invention provides the following technical scheme: preprocessing a collected cable image, and enhancing the image by adopting adaptive histogram equalization of an LAB color space; determining a scanning band at the center of the image and converting the scanning band to an HSV color space; the comprehensive value of the HSV color gradient is calculated, the peak value is detected by adopting an adaptive threshold value, and the boundary of each wire core is accurately positioned; establishing an HSV (hue, saturation and value) feature library containing 10 standard colors, and realizing color matching through weighted distance measurement; and an automatic detection mode and a manual assistance mode are provided. According to the method, the detection accuracy reaches 95% or above, the processing time of a single image is shorter than 1 second, the technical problems of automatic color recognition and sorting detection of the multi-core cable are effectively solved, and the method can be widely applied to cable production quality control.

The application is a kind of double robust causal dose-response curve estimation method based on high-dimensional independent variables, comprising the following steps: 1) constructing a new target function based on the modified adaptive LASSO method to realize dimension reduction; 2) constructing a double weighted distance correlation coefficient DWDC to select the optimal lambda n ; 3) using DR estimator to estimate the dose-response curve. The method mainly aims at the characteristics of the potential confounding variable set contained in the health medical big data, such as high dimension, nonlinear relationship between health outcome and (or) exposure factor, etc. In the framework of GOAL method, a double robust estimation method of causal dose-response curve is provided, which is called GOALDeR method. A large number of statistical simulations show that the estimation accuracy and precision of GOALDeR method are better than those of existing methods, and the method has double robustness and is less affected by the correlation structure between covariates and the n / p (sample size / covariate dimension) ratio.

The embodiment of the invention discloses a weld defect intelligent detection method and device based on multi-modal feature fusion. The method comprises the steps that an excitation unit is controlled to apply standardized mechanical knocking excitation to a target weld joint, and response signals generated by the weld joint are collected; transforming the time domain signals corresponding to the acoustic signal and the vibration signal based on Lie group transformation to obtain an invariant feature vector so as to construct a multi-modal feature vector; the weight of each feature in the multi-modal feature vector is determined based on the physical parameters of the target welding seam, and the weighted distance between the multi-modal feature vector and each clustering center in a standard database is calculated based on the weight, so that a target clustering center is determined; and in response to the fact that the target weighted distance corresponding to the target clustering center is larger than a distance threshold value, determining the defect type of the target weld joint based on the similarity between the multi-modal feature vector and each defect feature vector. In the embodiment of the invention, higher-precision and more reliable weld defect identification can be realized, and the whole process can be automatically carried out without manual operation.

The invention relates to an exploration data-driven three-dimensional space mineral migration path generation method, which comprises the following steps of: based on exploration data of a three-dimensional geological space, calculating distance and direction weighting of space points in a neighborhood of each voxel to obtain an optimal direction of each voxel; calculating a pairing point closest to each voxel in the optimal direction, and connecting the pairing points to form a migration path; according to the connection condition, the migration attribute of the space point is judged, and the migration attribute is a starting point, an end point or an intermediate point. The invention innovatively provides a migration path generation method oriented to deep three-dimensional space formation of minerals from a quantitative angle.

The present invention discloses a system and method for identifying a content of interest in a document. The method includes receiving a plurality of training documents that comprise a plurality of field elements, estimating for each data element present within the training document, a weighted distance of the each data element from a field element that corresponds to the content of interest. A feature vector is created based on the weighted distance and a position of the each data element with respect to the field element. A set of feature vectors are developed for the plurality of field elements and is used for training a field identification model. The field identification model is applied on the document to identify a beginning position of the content of interest.

A natural language processor extracts features from batches of unstructured traffic. A feature weighted distance engine computes a distance matrix between pairs of feature vectors for sessions of unstructured traffic using a weight vector that assigns importance to relative placement of features in feature vectors. The distance function used to compute the distance matrix with the weight vector is conducive to generating high-quality clusters and patterns in unstructured traffic. The sessions of unstructured traffic are clustered according to the pairwise distance matrix. Generated clusters are merged with clusters for previously analyzed sessions of unstructured traffic. A pattern identification engine extracts patterns from the merged clusters that correspond to behavior of applications generating the unstructured traffic.

The present invention belongs to the technical field of cottonseed oil production, and in particular relates to a data anomaly detection method for cottonseed oil production. First, sensor data from each link of raw materials, oil pressing, and refining are collected and preprocessed. Subsequently, the model clustering algorithm is improved, the cluster center is estimated, and an adaptive local weighted distance metric is introduced to construct a new distance formula. The membership matrix and the adaptive local weighted distance are iteratively calculated and substituted into the membership formula for dynamically adjusting the m value. At the same time, the cluster center set is optimized in combination with the simulated annealing idea, and the Frobenius norm is used to judge the convergence of the membership matrix, and the method is cycled until convergence. Finally, real-time production data is input based on the improved algorithm to detect anomalies. The method accurately responds to the complex working conditions of cottonseed oil production, improves the accuracy of data monitoring, effectively identifies abnormal data, provides key technical support for ensuring production stability, improving product quality and equipment safety, and helps enterprises optimize production management and enhance market competitiveness.

The application relates to the technical field of big model computing power scheduling, in particular to a computing power cooperative intelligent scheduling method and system based on a big model application, which comprises the following steps: obtaining a global computing power task request containing multiple big model inference tasks, extracting multi-dimensional features according to task constraint conditions and forming a task demand vector. An improved greedy algorithm is adopted to match the task demand vector and a state vector of a dynamic heterogeneous computing power resource pool, a preliminary scheduling instruction set is generated through weighted distance, a multi-step forward-looking evaluation model with computing power resource balance and task communication overhead as joint targets is constructed, the running state of tasks and resources in multiple time slices is simulated, the scheduling instruction is corrected, and finally the instruction is issued to a hardware computing unit to complete task execution. The method can improve the matching accuracy of tasks and computing power resources, optimize the balance of resource allocation, and reduce the data communication overhead between tasks.

This invention discloses a method for optimizing the design of complex equipment based on a hybrid adaptive sampling surrogate model. This method divides the design space into several Voronoi polygons controlled by sample points. Three indices—leave-one-out error, local nonlinearity, and polygon region size—are used to evaluate the importance of each sample point region. An entropy-weighted distance method is used to select the most suitable sensitive region for adding points. A learning function based on error estimation is then combined to determine new sampling points. This iterative update constructs a high-precision surrogate model, establishing a mapping relationship between design parameters and optimization objectives, thereby solving the design optimization problem. This invention innovatively proposes a multi-index adaptive weight allocation method to select sensitive sample regions and proposes a novel learning function to determine the location of added points. This method can construct a high-precision surrogate model using fewer sample points and can be used for the optimization design of complex equipment, including but not limited to tunnel boring machines.

The invention discloses a Chinese character font style consistency discrimination method based on a twin network. The method comprises the steps of obtaining different character images and performing preprocessing; inputting the character image into a VGG16 network, and extracting a feature vector of the character image through a convolutional layer and an ECA attention mechanism; optimizing VGG16 network parameters by adopting a binary cross entropy loss function; and processing the feature vector by using an adaptive stroke weighted distance measurement method, calculating a distance value, and comparing the distance value with a set similarity threshold to obtain a discrimination result. The problem of style consistency evaluation in new font generation is effectively solved, and compared with a traditional evaluation mode, a large amount of manpower and time cost are saved.

The invention discloses an auxiliary management method and system for rattan plaiting skill data based on image analysis, and relates to the technical field of rattan plaiting image processing.The method comprises the steps that an initial plaiting topological graph is constructed by obtaining an original rattan plaiting image, and high-confidence and interference topology first-level sub-graphs are divided through pixel quality evaluation; taking the adjacent high-confidence topology sub-graphs as reference, calculating the similarity between the adjacent boundary length and the topological structure and coding a structure prior vector, and selecting weighted fusion according to vector dispersion or dividing into interference topology secondary sub-graphs according to weighted distance so as to determine generation prior; topological sub-graphs are repaired through a conditional generation model, a complete woven topological graph is output through fusion, and precision can be improved through optimization of time sequence, symmetry and boundary consistency. According to the method, the topological structure of the interference area can be accurately repaired, and the integrity and reliability of the rattan-plaited woven topological graph are remarkably improved.

The invention relates to the technical field of mobile terminal safety monitoring, and discloses a risk signal screening monitoring method and system for a mobile terminal. The method comprises the following steps: collecting intensity change sequences of various risk signals of the mobile terminal, calculating cosine similarity and propagation momentum factors of deviation rate vectors, counting time sequence mutual information of the risk signals to determine a causal propagation relationship, constructing a directed propagation graph to predict a future risk evolution value, and calculating the risk evolution value of the mobile terminal. And calculating a weighted distance, dynamically adjusting a judgment threshold and judging a multi-step progressive attack. The technical problems that in the prior art, staged progressive attacks cannot be recognized, risk evolution cannot be pre-judged in advance, the detection sensitivity cannot be adaptively adjusted, and the response strategy is extensive and single are solved. According to the method and the device, the recognition accuracy and the response timeliness of the multi-step progressive Trojan horse attack are improved.

The invention relates to the technical field of biodiversity research, and discloses a mountain tree species diversity remote sensing estimation method, which comprises the following steps: acquiring mountain tree species distribution data, acquiring a mountain remote sensing image, preprocessing the data, and calculating a tree species diversity index according to tree species distribution. Calculating the correlation between the spectral heterogeneity index and the tree species diversity index, constructing a mountain tree species diversity remote sensing mapping model according to the influence factor calculated by the spectral heterogeneity index, extracting the optimal parameter of mountain tree species diversity remote sensing estimation, and making a tree species diversity index spatial distribution map according to the spectral heterogeneity index to realize tree species diversity estimation. A terrain correction mechanism is introduced into the spectral heterogeneity index constructed by the method, the stability and reliability of the spectral heterogeneity index are improved, spectral response of the model to different tree species is improved after a weighted distance construction mechanism between pixels is combined, and the method is suitable for mountain forest areas with obvious topographic relief and has good application prospects. And multi-source input of multi-sensor remote sensing images is supported.

The invention relates to the technical field of computers, in particular to a Bayesian structure learning method fusing causal analysis and swarm intelligence, and the method comprises the steps: introducing a common feature proportion weighted distance and similarity weighted voting mechanism, screening father and child node sets of a target variable in combination with an HITON-PC algorithm, dynamically adjusting a neighborhood weight, and optimizing an iterative convergence strategy, improving a KNN filling algorithm, and providing an EIKNN algorithm for filling missing data; based on the filled data set, analyzing a causal relationship among variables in the data set by using a scaling framework, and generating a corresponding initial population; and carrying out deep fusion on an EIKNN algorithm, a scaling framework and a human evolutionary optimization algorithm, proposing an ECS-HEOA algorithm, and optimizing the Bayesian structure by using the ECS-HEOA algorithm to obtain an optimal structure. And a new thought and technology are provided for Bayesian structure learning of high-dimensional data and complex missing scenes.

The invention relates to the technical field of hydrology and water quality on-line monitoring, in particular to a hydrology and water quality on-line monitoring data interpolation method based on double-circulation weighting, which comprises the following steps of: 1) determining the interpolation sequence of each parameter and missing value of hydrology and water quality data by using information entropy; and 2) restoring a missing value by using a complete parameter and instance pool, establishing a parameter weighting matrix, weighting the Euclidean distance, and calculating a local fluctuation error. The method has the advantages that the interpolation method based on the K-nearest neighbor algorithm (KNN) is designed, the interpolation performance is improved by adopting weighted distance measurement and increasing fluctuation errors, increment loop learning is performed by utilizing variable and instance information, original data features are reserved as far as possible, meanwhile, a more accurate interpolation result is provided, and the method is suitable for large-scale popularization and application. The method is an effective tool for processing hydrology and water quality data with multiple missing modes.