Using multiple linear regression model to estimate thunderstorm activity

Suparta, Wayan; Putro, Wahyu Sasongko

doi:10.1088/1757-899x/185/1/012023

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The prediction of thunderstorm occurrence for each season using the Jacobi method and the standard deviation error. The data used is from Suparta and Putro (2017).…”

Section: Solutionmentioning

confidence: 99%

Prediction of Thunderstorm Occurrences in Tropical Areas Using a Numerical Model

Suparta¹,

Putro²,

Darmastono³

2021

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The prediction of thunderstorm activity is not only significant for weather forecasting but also for the standardization of risk assessment as in the aviation industry or emergency unit purposes. This paper aimed to develop a prediction of thunderstorm occurrences using a nonlinear model. For this work, the data used for a case study is one-year (1 January 2012 to 31 December 2012) located in a tropical area. The Jacobi algorithm has been employed to construct a prediction model with six combinations of input and one output (target). The predicted target is thunderstorm occurrence. The parameter input is surface pressure, air temperature, relative humidity, clouds, precipitable water vapor, and precipitation. The result obtained a better fit prediction model with four optimum parameters and estimation errors of 5.73%. May and October are the highest occurrences of thunderstorms where prediction errors were found high during the intermonsoon season.

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“…The prediction of thunderstorm occurrence for each season using the Jacobi method and the standard deviation error. The data used is from Suparta and Putro (2017).…”

Section: Solutionmentioning

confidence: 99%

Prediction of Thunderstorm Occurrences in Tropical Areas Using a Numerical Model

Suparta¹,

Putro²,

Darmastono³

2021

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“…A significant number of studies have been performed on linear regression [8], [9], [10]. One is [11] analyzes to determine clean water specifications.…”

Section: Introductionmentioning

confidence: 99%

“…From the results of the study, it is possible when the clean water need is 12,883,649,061 M3 for 2018-2021 and the number of users increases by 21005 in 2021, to apply and enforce the linear regression algorithm. Additional studies [8] are on storm activity predictions. In this paper, a numerical model is built using nonlinear models to estimate the tempest behavior via linear regression.…”

Section: Introductionmentioning

confidence: 99%

Forecast Analysis of Gross Regional Domestic Product based on the Linear Regression Algorithm Technique

Pasaribu¹,

Septiani²,

Rahayu³

et al. 2021

TEM Journal

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Statistical data are indispensable for macro-economic planning activities such as the Gross Regional Domestic Product (GRDP) where data can determine the economic development strategies and policies that have been adopted and can be continued in the future. This study draws on quantitative data sources from the Regional Statistical Agency of Jakarta for the period 2017-2019, the subject of the Gross Regional Domestic Product based on current business prices. The aim of this research is to test and predict the level of accuracy of GRDP at current prices based on business fields using the Linear Regression method supported by Rapid Miner software. The results show that the validated Linear Regression algorithm with K-Fold values from 2 to 10 with the sampling type linear sampling and shuffled sampling can be used and implemented with the smallest Root Mean Square Error value of IDR 9,977,431 at k = 10 for the sampling.

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“…Faridah Hani Mohamed et al [6] devised a multiple regression model for recreation of gene regulatory networks to solve cascade error problems which gave a satisfactory result with AUROC values above 0.5. Supatra and Putro [7] designed a model based on linear regression to estimate thunderstorm activity which takes six inputs and gives one output with below 50% error and maximum epoch reaching up to 1000 iterations. Altay and Satman [8] induced artificial neural network and linear regression in their stock market forecasting and compared the results and found out that ANN models performed much better than regression models.…”

Section: Introductionmentioning

confidence: 99%

Application of Backward Elimination in Multiple Linear Regression Model for Prediction of Stock Index

Mishra

Mohanty

Rath

et al. 2020

Smart Innovation, Systems and Technologies

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Technological progress raises the stock and shares market analysis. Specific mathematical and machine learning methods improve decision-making. Major research work focused on the stock price forecast feature based on historical rates and volume. In this work, TCS stock index analyzes the performance measurements using statistical methods in Python environment. In this analysis, the results obtained are superior to the existing methods. The methods for analyzing the financial market are based on a multiple linear regression using backward elimination method. This paper focuses on the best independent variables to forecast the stock market's closing price. This research is used to find specific variables that show the greatest effect on closing price prediction. Keywords Stock market • Linear regression • Multiple linear regression •

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Using multiple linear regression model to estimate thunderstorm activity

Cited by 5 publications

References 5 publications

Prediction of Thunderstorm Occurrences in Tropical Areas Using a Numerical Model

Prediction of Thunderstorm Occurrences in Tropical Areas Using a Numerical Model

Forecast Analysis of Gross Regional Domestic Product based on the Linear Regression Algorithm Technique

Application of Backward Elimination in Multiple Linear Regression Model for Prediction of Stock Index

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