Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS

Suparta, Wayan; Putro, Wahyu Sasongko

doi:10.11591/eei.v6i2.648

Cited by 9 publications

(8 citation statements)

References 11 publications

(16 reference statements)

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“…This shows that the Ordinary Least Square method is more capable of providing a more accurate output due to the algorithm allowing us to find a gradient that is close as possible to the dependent variables despite 1506 having an only above average prediction. In the future, we hope to inevestigate the impact of using multiple regression models such as in [23] and [24] or other regression-based models [25].…”

Section: Discussionmentioning

confidence: 99%

A regression approach for prediction of Youtube views

et al. 2019

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YouTube has grown to be the number one video streaming platform on Internet and home to millions of content creator around the globe. Predicting the potential amount of YouTube views has proven to be extremely important for helping content creator to understand what type of videos the audience prefers to watch. In this paper, we will be introducing two types of regression models for predicting the total number of views a YouTube video can get based on the statistic that are available to our disposal. The dataset we will be using are released by YouTube to the public. The accuracy of both models are then compared by evaluating the mean absolute error and relative absolute error taken from the result of our experiment. The results showed that Ordinary Least Square method is more capable as compared to the Online Gradient Descent Method in providing a more accurate output because the algorithm allows us to find a gradient that is close as possible to the dependent variables despite having an only above average prediction.

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Section: Discussionmentioning

confidence: 99%

A regression approach for prediction of Youtube views

et al. 2019

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“…Adaptive neuro-fuzzy inference system (ANFIS) combines the merits of fuzzy as well as the adaptability of ANN. ANFIS approach is used as a teaching method for sugeno-type fuzzy systems subjected to the following limitations [37][38][39][40]: The user is required to provide ANFIS by number of membership functions (MFs) for each input and output, the MFs' type, the number of training and checking data, and the optimization criterion for reducing the measured error. Normally, the optimization criterion is defined by the number of the squared difference between the actual and lineari zed N curve [40][41].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

Abo‐Elyousr

Nassrat

2018

TELKOMNIKA

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Polluted insulators at high voltages has acquired considerable importance with the rise of voltage transmission lines. The contamination may lead to flashover voltage. As a result, flashover voltage could lead to service outage and affects negatively the reliab ility of the power system. This paper pr esents a dynamic model of ac 50Hz flashover voltages of polluted hydrophobic polymer insulators. The models are constructed using the regression tree method, artificial neural network (ANN), and adaptive neuro -fuzzy (ANFIS). For this purpose, more than 2000 different experimental testing conditions were used to generate a training set. The study of the ac flashover voltages depends on silicone rub ber (SiR) percentage content in ethylene propylene diene monomer (EPDM) rub b er. Besides, water conductivity (µS /cm), numb er of droplets on the surface, and volume of water droplet (ml) are considered. The regression tree model is ob tained and the performance of the proposed system with other intelligence methods is compared. It can b e concluded that the performance of the least squares regression tree model outperforms the other intelligence methods, which gives the proposed model b etter generalization ab ility.

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“…Addressing these challenges depends on breakthroughs in environmental policy and climate science [2]. Climate research is key to understanding the long-term effects of global warming on agriculture [3][4], food security [5], air quality [6], and weather conditions [7]. On the other hand, one of the primary data sources that empower environmental research is satellite imagery [8].…”

Section: Introductionmentioning

confidence: 99%

Satellite image inpainting with deep generative adversarial neural networks

Zaytar

Amrani

2021

IJ-AI

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This work addresses the problem of recovering lost or damaged satellite image pixels (gaps) caused by sensor processing errors or by natural phenomena like cloud presence. Such errors decrease our ability to monitor regions of interest and significantly increase the average revisit time for all satellites. This paper presents a novel neural system based on conditional deep generative adversarial networks (cGAN) optimized to fill satellite imagery gaps using surrounding pixel values and static high-resolution visual priors. Experimental results show that the proposed system outperforms traditional and neural network baselines. It achieves a normalized least absolute deviations error of ( & decrease in error compared with the two baselines) and a mean squared error loss of ( & decrease in error) over the test set. The model can be deployed within a remote sensing data pipeline to reconstruct missing pixel measurements for near-real-time monitoring and inference purposes, thus empowering policymakers and users to make environmentally informed decisions.

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Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS

Cited by 9 publications

References 11 publications

A regression approach for prediction of Youtube views

A regression approach for prediction of Youtube views

Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

Satellite image inpainting with deep generative adversarial neural networks

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