Weather Visibility Prediction Based on Multimodal Fusion

In this research, a weather forecasting model based on machine learning is proposed for improving the accuracy and efficiency of forecasting. The aim of this research is to propose a weather prediction model for short-range prediction based on numerical data. Daily weather prediction includes the work of thousands of worldwide meteorologists and observers. Modernized computers make predictions more precise than ever, and earth-orbiting weather satellites capture pictures of clouds from space. However, in many cases, the forecast under many conditions is not accurate. Numerical weather prediction (NWP) is one of the popular methods for forecasting weather conditions. NWP is a major weather modeling tool for meteorologists which contributes to more accurate accuracy. In this research, the weather forecasting model uses the C5.0 algorithm with K-means clustering. The C5.0 is one of the better decision tree classifiers, and the decision tree is a great alternative for forecasting and prediction. The algorithm for clustering the K-means is used to combine identical data together. For this process, the clustering of K-means is initially applied to divide the dataset into the closest cluster of K. For training and testing, the meteorological data collection obtained from the database Modern-Era Historical Analysis for Research and Applications (MERRA) is used. The model's performance is assessed through MAE mean absolute error (MAE) and root mean square error (RMSE). And the proposed model is assessed with accuracy, sensitivity, and specificity for validation. The results obtained are compared with other current machine learning approaches, and the proposed model achieved predictive accuracy of 90.18%.

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“…The k-means algorithm was one of the basic clustering methods and is commonly utilized in many fields. 16 The value of k is 2.…”

Section: K-means Clusteringmentioning

confidence: 99%

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

138

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“…The best performance is related to XGB with a bias of − 144 m, a mean absolute error of 1322 m with 0.88 correlation and a root-mean-square error of 2013 m. The mean absolute relative error is about 47%. Zhang et al [40] stated that by fusing multimodal information (combining XGBoost and LightGBM), the visibility prediction system can be significantly improved compared to XGBoot-and LightGBM-based models separately. But, the developed model has a higher root-mean-square error (6710 m) in comparison with our finding (2013 m).…”

Section: Models Global Performance Comparisonmentioning

confidence: 99%

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Bari

Ouagabi

2020

SN Appl. Sci.

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Low-visibility conditions are a weather hazard that affects all forms of transport, and accurate forecasting of their spatial coverage is still a challenge for meteorologists, particularly over a large domain. Current predictions of visibility are based on physical parametrizations in mesoscale models and are thus limited with respect to accuracy. This paper examines the use of supervised machine-learning regression techniques (tree-based ensemble, feed-forward neural network and generalized linear methods) to diagnose visibility from operational mesoscale model forecasts over a large domain. To achieve this, hourly forecasts of meteorological parameters in the lower levels of the atmosphere have been used. In the short-range forecasting framework, the machine-learning algorithms were developed to provide hourly forecasts up to 24 h. To assess the performance of the developed models, hourly observed data, collected at 36 synoptic land stations over the northern part of Morocco, have been used. This region is characterized by a heterogeneous topography. The tree-based ensemble methods have shown some improvement in visibility forecasting in comparison with the operational visibility diagnostic scheme based on Kunkel's formula and also with persistence. It is also found that this machine-learning technique performs better when the forecast depends on multiple predictors instead of only a few with very high importance. In addition, their performance is very sensitive to the disproportionality of data availability between daytime and night-time. Furthermore, it is found that the performance decreases when principal components are used instead of raw correlated data.

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“…LightGBM has a maximum depth parameter, it expands like a tree but prevents overfitting. Gradient boosting, due to its tree structure, is known to be good for tabular data but recently researchers have found it useful in a various applications [55][56][57][58][59][60][61][62][63][64][65][66][67].…”

Section: Why Lightgbm?mentioning

confidence: 99%

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

Gupta

Singh²,

Sinha³

2020

Preprint

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Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.

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Weather Visibility Prediction Based on Multimodal Fusion

Cited by 34 publications

References 32 publications

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

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