Using hydrodynamic models to carry out early warning and flash floods forecasting is an essential measure for loss reduction. Nevertheless, many current hydrodynamic models lack the necessary forecasting timeliness. T...
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Using hydrodynamic models to carry out early warning and flash floods forecasting is an essential measure for loss reduction. Nevertheless, many current hydrodynamic models lack the necessary forecasting timeliness. To address this limitation, a method combining a hydrodynamic model with the K nearest neighbours (knn) algorithm is proposed to facilitate the rapid prediction of flash flood processes. With the rainfall sequence as the input data and the simulation results of the hydrodynamic model as the target data, the rapid forecast of water depth, water velocity and discharge are achieved. Then the Baogai Temple basin is utilized as a case study, and the rapid forecast model (RFM) is established and subjected to verification for reliability and timeliness. The results demonstrate that the established model exhibits remarkable accuracy, with 99% of the test data effectively limiting the error of accumulated inundation extent within 20%. Furthermore, the Nash-Sutcliffe efficiency (NSE) for cross-sectional discharge achieves a value of 0.98. In 75% of rainfall scenarios, both the maximum average water depth and velocity errors for the cross-sections are effectively confined to 7.5% and 10%, respectively. The model also boasts a substantial improvement in computational efficiency, enabling it to complete the prediction of the flooding process for the next 10 h within 25s. This enhancement offers valuable lead time for emergency decision-making and highlights its extensive application potential in managing flash floods.
In the era of big data, libraries manage huge electronic text resources, of which English text resources are particularly critical for academic research, student learning, and professional knowledge acquisition. This ...
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In the era of big data, libraries manage huge electronic text resources, of which English text resources are particularly critical for academic research, student learning, and professional knowledge acquisition. This paper aims to improve the K-nearest neighbor algorithm and design an intelligent classification model to improve the efficiency and quality of library services. An improved method based on in-class K-means clustering and class mean distance is used to characterize and extract text information with a vector space model. The results showed that the improved K-nearest neighbor algorithm achieved significant improvement in the precision, recall, and F1 values, reaching 90.50 %, 89.95 %, and 89.37 %, respectively. The classification time was significantly reduced to 1034.57 s. In addition, the improved algorithm had a classification accuracy of 94 %, surpassing other popular text classification algorithms. The research successfully realizes the efficient classification of text. The research results not only improve the classification efficiency of library English text resources but also provide strong support for readers to quickly obtain the required information, which has important application value and wide application prospects.
In recent years, brain tumors have become one of the most common fatal diseases. Despite the existence of an important number of research studies on tumors, the proportion of research on predicting the growth of tumor...
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In recent years, brain tumors have become one of the most common fatal diseases. Despite the existence of an important number of research studies on tumors, the proportion of research on predicting the growth of tumors remains insufficient due to the intricate nature of this research domain. Therefore, the presence of any application able to predict the growth of the tumor may have a role in eliminating the tumor by finding the appropriate treatment for it before it grows. This paper investigates tumor growth and presents a technique for tumor growth prediction based on the Discrete Time Markov Chain (DTMC) and K-Nearest Neighbor (knn) algorithms. The design and development of this technique consists of a proposition of a stochastic model of tumor progression. This is followed by an extension of the mode to several cases that allow the derivation of new cases based on the study of predictive probabilities. The aim of this paper is to develop a model based on the knn and DTMC algorithms that can classify tumors and predict the future state based on the current state of the tumor without the knowledge of the past state. In other words, all relevant information about the past and the present that would be useful in making predictions is available in the current state. In terms of performance evaluation metrics, the results show that the proposed method exceeds the existing methods with 97.65% accuracy, 71.65% specificity and 99.087% sensitivity.
Physiological state abnormality due to genetic diseases, excessive exercise, etc. is becoming a fatal killer endangering people's life and safety because of its hidden characteristics. K-Nearest Neighbor(knn) Algo...
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ISBN:
(数字)9798350354621
ISBN:
(纸本)9798350354638;9798350354621
Physiological state abnormality due to genetic diseases, excessive exercise, etc. is becoming a fatal killer endangering people's life and safety because of its hidden characteristics. K-Nearest Neighbor(knn) algorithm is widely used in various fields due to its simple implementation, but when the sample capacity is too large or the feature attributes are too many, the classification efficiency decreases significantly. This paper proposed an improved knn(Iknn) algorithm based on clustering by hierarchically clustering the data in data pre-processing, which reduced the search space of the algorithm and effectively improved the search efficiency. When the improved knn algorithm was applied in the physiological state abnormality discrimination field, which better improved the efficiency and accuracy of physiological abnormality discrimination. Results show that this could provide an effective guarantee for the early discovery of physiological parameter abnormality symptom, the timely adoption of dispositive measures, and the maintenance of people's life safety.
Attendance management within educational settings is undeniably vital, but the conventional manual method poses several issues, including time consumption and vulnerability to proxy attendance. To address these challe...
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In this study, a visual power distribution room fault detection system based on knn algorithm and Unity3D engine is proposed. The simulation system is designed to provide a realistic, interactive environment for simul...
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Classification and identification is of great significance in biological sequence analysis research, which can help to solve the problems of functional identification, medical diagnosis, epidemic monitoring and so on....
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By analyzing and processing user consumption behavior data on agricultural e-commerce platforms, selecting the appropriate algorithm - knn algorithm, conducting cluster analysis on these data, analyzing the consumptio...
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By analyzing and processing user consumption behavior data on agricultural e-commerce platforms, selecting the appropriate algorithm - knn algorithm, conducting cluster analysis on these data, analyzing the consumption habits of each user, and then classifying these users based on this result to understand and master different product attributes and user characteristics. Finally, predicting the market value of each product inside can provide more accurate marketing decision support for agricultural e-commerce platforms and promote their development. This study shows that the application of knn algorithm in precision marketing of agricultural product e-commerce plays an important role and can provide important reference for the formulation of marketing strategies.
In this paper, given the mindset of data analysis, we adopt the knn algorithm based on the data-driven principle to research and judge the stock price trend. We use a weighted modified Euclidean distance algorithm to ...
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Distribution transformers are significant components in a grid utility system. Therefore, knowing the health status of a transformer becomes vital for the safe operation of a grid. Health Index (HI) defined the health...
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ISBN:
(纸本)9781665400916
Distribution transformers are significant components in a grid utility system. Therefore, knowing the health status of a transformer becomes vital for the safe operation of a grid. Health Index (HI) defined the health status based on laboratory tests and interpreted by the standards specified by IEEE, IEC. The obtained HI number represents whether a transformer is healthy and able to operate further or unhealthy and needs a replacement. In this paper, the k Nearest Neighbors (knn) algorithm is employed to predict the health status based on the laboratory test data. A total of six tests performed on a class of distribution transformers are used as predictors. The knn algorithm has proved to be a good classifier for the transformer health status prediction problem by achieving good accuracy.
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