A novel approach for precipitation forecast via improved K-nearest neighbor algorithm

Huang, Mingming; Lin, Runsheng; Huang, Shuai; Xing, Tengfei

doi:10.1016/j.aei.2017.05.003

Cited by 67 publications

(39 citation statements)

References 17 publications

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“…Inspired by the effectiveness of the exponential of some distance for classification, we believe that this approach should be a better choice as the weighting scheme. Also, we proposed an improved KNN algorithm from the literature [18] to predict the rainfall grade, and found that the performance of our proposed rainfall grade approach based on our improved KNN algorithm is somewhat better than the other three approaches, namely, DWKNN, WKNN and KNN.…”

Section: Introductionmentioning

confidence: 94%

Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

et al. 2019

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The prediction method plays crucial roles in the accurate prediction of rockfall runout range which could improve the protection of endangered residential areas and infrastructure. Recently, the Knearest neighbor (KNN) algorithm, one of many machine learning techniques, showed good performance in pattern classification. Therefore, the aim of this study was to use the K-nearest neighbor (KNN) algorithm to predict the rockfall runout range which is classified into different subintervals according to the distance from the slope toe. First, we proposed the prediction model of the rockfall runout range based on our improved KNN algorithm which could better offer robustness against different choices of the neighborhood size k, and it is the first work of applying our improved KNN algorithm to rockfall runout range prediction. Second, the shaking table tests of rockfall runout models were conducted for simulating the rockfall process, and the influence laws of factors-including types of an earthquake, peak ground acceleration, vibration frequency, slope angle, slope height, and block mass and block shape-on rockfall distance are investigated. Finally, there is a discussion of the performance of our proposed prediction model based on our improved KNN algorithm in the prediction of rockfall runout range. The extensive experimental results for rockfall runout range prediction demonstrate the effectiveness of our proposed prediction model. INDEX TERMS Improved KNN algorithm, rockfall runout range, earthquake, shaking table test.

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Section: Introductionmentioning

confidence: 94%

Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

et al. 2019

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“…The KNN has been used in several remote sensing applications such as precipitation types classifications (e.g. Yang et al, 2019), precipitation estimation (Ahmed et al, 2020;Huang et al, 2017), and image classification (e.g. Li et al, 2009) etc.…”

Section: Model Selectionmentioning

confidence: 99%

Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods

Adhikari

Ehsani

Song

et al. 2020

Earth and Space Science

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Accurate quantification of snowfall rate from space is important, but has remained difficult. Four years (2007-2010) of NOAA-18 Microwave Humidity Sounder (MHS) data are trained and tested with snowfall estimates from coincident CloudSat Cloud Profiling Radar (CPR) observations using several machine learning methods. Among the studied methods, random forest using MHS (RF-MHS) is found the best for both detection and estimation of global snowfall. The RF-MHS estimates are tested using independent years of coincident CPR snowfall estimates and compared with snowfall rates from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), Atmospheric Infrared Sounder (AIRS), and MHS Goddard Profiling Algorithm (GPROF). It was found that RF-MHS algorithm can detect global snowfall with approximately 90% accuracy and a Heidke skill score of 0.48 compared to independent CloudSat samples. The surface wet bulb temperatures, brightness temperatures at 190 GHz, and 157 GHz channels are found to be the most important features to delineate snowfall areas. The RF-MHS retrieved global snowfall rates are well compared with CPR estimates and show generally better statistics than MERRA-2, AIRS, and GPROF ©2020 American Geophysical Union. All rights reserved. products. A case study over the US verifies that the RF-MHS estimated snowfall agrees well with the ground-based NCEP Stage-IV and MERRA-2 product whereas a relatively large underestimation is observed with the current GPROF product (V05). MHS snowfall estimated based on RF algorithm, however, shows some underestimation over cold and snow-covered surfaces (e.g., Greenland, Alaska, and Northern Russia), where improvements through new sensors or retrieval techniques are needed.

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“…In this section, our proposed prediction model is trained by 50 typical slope stability cases and tested by 14 typical slope stability cases. e neighborhood size k ranges from 1 to 7 with an interval of 1, which is inspired by [25]. e 50 typical slope stability cases are shown in Table 3, and the 14 typical slope stability cases are shown in Table 4.…”

Section: Procedures Algorithm Of Our Proposed Prediction Modelmentioning

confidence: 99%

An Improved KNN‐Based Slope Stability Prediction Model

Huang

Lyu

2020

Advances in Civil Engineering

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An accurate slope prediction model is important for slope reinforcement before the disaster. The k-nearest neighbor (KNN) algorithm, as a simple and effective nonparametric machine learning method, is widely applied in classification recognition. In our study, the k-nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k-nearest neighbor (KNN) algorithm. Extensive experimental results show that our proposed prediction model achieves high prediction performance in this regard. Moreover, a comparison between our proposed prediction model and the finite element method, which is the classical theoretical method of slope stability, was made, which will provide an important approach to predicting the slope stability for slope engineering. Finally, shaking table test of a slope model is conducted to evaluate whether the slope is stable or not, and the experimental results are in good agreement with the prediction results of our proposed prediction model, which further demonstrates its effectiveness.

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A novel approach for precipitation forecast via improved K-nearest neighbor algorithm

Cited by 67 publications

References 17 publications

Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods

An Improved KNN‐Based Slope Stability Prediction Model

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