A Hybrid Fuzzy and K-Nearest Neighbor Approach for Debris Flow Disaster Prevention

Su, Te‐Jen; Pan, Tzung-Shiarn; Chang, Yung-Lu; Lin, Shou-Sheu; Hao, Miin-Jong

doi:10.1109/access.2022.3152906

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…The k-nearest neighbours algorithm is based on the principle of determining the distance of k-nearest neighbours to a certain point [48]. Here, the k parameter may vary according to the model [49,50].…”

Section: K-nearest Neighbours (Knn)mentioning

confidence: 99%

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

et al. 2023

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Energy efficiency and operational safety practices on ships have gained more importance due to the rules set by the International Maritime Organization in recent years. While approximately 70% of the fuel consumed on a commercial ship is utilized for the propulsion load, a significant portion of the remaining fuel is consumed by the auxiliary generators responsible for the ship’s onboard load. It is crucial to comprehend the impact of the electrical load on the ship’s generators, as it significantly assists maritime operators in strategic energy planning to minimize the chance of unexpected electrical breakdowns during operation. However, an appropriate handling mechanism is required when there are massive datasets and varied input data involved. Thus, this study implements data-driven approaches to estimate the load of a chemical tanker ship’s generator using a 1000-day real dataset. Two case studies were performed, namely, single load prediction for each generator and total load prediction for all generators. The prediction results show that for the single generator load prediction of DG1, DG2, and DG3, the decision tree model encountered the least errors for MAE (0.2364, 0.1306, and 0.1532), RMSE (0.2455, 0.2069, and 0.2182), and MAPE (17.493, 5.1139, and 7.7481). In contrast, the deep neural network outperforms all other prediction models in the case of total generation prediction, with values of 1.0866, 2.6049, and 14.728 for MAE, RMSE, and MAPE, respectively.

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Section: K-nearest Neighbours (Knn)mentioning

confidence: 99%

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

et al. 2023

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“…S UPERVISED learning tasks involve the use of functions that map inputs to outputs based on samples in pairs form, i.e., inputs and outputs. The k-nearest neighbours (kNN) technique is recognized as one of the top ten algorithms in machine learning used for supervised learning (classification and regression) [1]- [6]. It predicts the response value of a new point by identifying a set of k observations in the neighbourhood, aiming to minimize the impact of outliers in the training data.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Random Projection k Nearest Neighbors Ensemble via Extended Neighborhood Rule for Binary Classification

Ali,

Khan,

Muhammad Khan

et al. 2024

IEEE Access

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This paper presents an ensemble method for binary classification, where each base model is based on an extended neighbourhood rule (ExNRule). The ExNRule identifies the neighbours of an unseen observation in a stepwise manner. This rule first selects the sample point closest to the experimental observation and then selects the second observation nearest to the previously chosen one. To find the required data points in the neighbourhood, this search is repeated up to k steps. The test sample point is predicted using majority voting in the class labels of the k chosen neighbours. In the proposed method, a large number of ExNRule based models are constructed on randomly projected bootstrap samples. The error rates of these models are computed using out-of-bag data points. The models are then ranked according to their out-ofbag errors, and a proportion of the most accurate models are selected. The final ensemble is constructed by combining the selected models. The proposed method is compared with other classical procedures on 15 benchmark datasets in terms of classification accuracy, Kohen's kappa and Brier score (BS) as performance metrics. Boxplots of the results are also constructed. The proposed ensemble is outperforming the existing methods on almost all the benchmark datasets. For further evaluation, the proposed method is compared with other kNN based classifiers on 3 datasets using different k values. Furthermore, the performance of the proposed method is also evaluated using simulated data under different scenarios.

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“…According to statistics, the direct economic loss caused by debris ow disasters in China exceeds 10 billion yuan every year, accounting for more than 10% of the total loss of natural disasters [1 ~ 3]. Therefore, it is of great signi cance to accurately predict and evaluate the occurrence, development, and impact of debris ow disasters for the prevention and mitigation of debris ow disasters [4,5].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Debris Flow Disaster in Yunnan Mountainous Areas Guided by Discrete Element Tracing Method

He,

Li,

Liu

2023

Preprint

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The purpose of this study is to accurately predict and evaluate the occurrence, development, and impact of debris flow disasters, and to further improve the accuracy of debris flow disaster prediction by comparing the simulation results of high-performance algorithms with the measured data and other numerical simulation methods. Discrete Element Tracking Method (DETM) is adopted as a numerical simulation method. Debris flow is regarded as a non-Newtonian fluid composed of many discrete particles, and the motion state and deformation characteristics of debris flow are calculated by tracking the position, velocity, and force of each particle. This study takes a typical debris flow channel in Yunnan Province as an example. Firstly, a three-dimensional (3D) terrain model is established, including the length, width, slope, and curvature of the channel. Secondly, according to the physical characteristics of debris flow, the initial conditions of debris flow are set. Finally, the movement process of debris flow is simulated by DETM, and the position, speed, and force of each particle in the process of debris flow movement are tracked. The numerical simulation results are compared with those of the Finite Element Difference Method (FEDM), and the simulation results are checked with the data in the national debris flow database. It is found that the coincidence degree of debris flow deposition range guided by DETM and debris flow database is 0.89 (FEDM is 0.76). The root mean square error (RMSE) of debris flow deposition thickness and debris flow database is 0.04 (FEDM is 0.23). The relative error of debris flow deposition volume and debris flow database is 0.06 (FEDM is 0.15). The relative error of debris flow movement time and debris flow database is 0.03 (FEDM is 0.19). These results show that DETM can well predict the actual situation of debris flow disasters in mountainous areas of Yunnan. This study not only provides a new tool and basis for the prediction and prevention of debris flow disasters, but also provides a new idea and method for the application of DETM in simulating other non-Newtonian fluids.

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A Hybrid Fuzzy and K-Nearest Neighbor Approach for Debris Flow Disaster Prevention

Cited by 11 publications

References 9 publications

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

An Optimal Random Projection k Nearest Neighbors Ensemble via Extended Neighborhood Rule for Binary Classification

Numerical Simulation of Debris Flow Disaster in Yunnan Mountainous Areas Guided by Discrete Element Tracing Method

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