Prediction of Climate Variable using Multiple Linear Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

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“…Sreehari. [46] A described article explains MLR rainfall prediction. It will help farmers determine crop yields.…”

Section: Iiia Review On (Linear Regression)mentioning

confidence: 99%

A Review on Linear Regression Comprehensive in Machine Learning

Maulud

Abdulazeez

2020

JASTT

655

145

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“…This is one of the most significant issues that farmers face during the entire crop life cycle. The analysis conducted on real-time meteorological data in order to anticipate and improve agricultural productivity shows that the predictions are still challenging because of dynamic changes [46] [47].…”

Section: Challenges and Limitations In Predicting The Crop Yieldmentioning

confidence: 99%

A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

Prabavathi¹,

Chelliah²

2022

ujar

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Agriculture is the backbone of India's economy, as it is the most important factor in the country's socio-economic development. Because of the rapid expansion in human population, the "Green Revolution" introduced high yield variety (HYV) seeds, which increased crop productivity but degraded crop and soil quality. This is due to the use of excessive amounts of chemical fertilizers in HYV seeds, as well as the irrigation system utilized to grow these seeds. This stunts the growth of the crops, resulting in financial and productivity losses. Because of field surveys, traditional ways to crop production prediction will take longer, and contemporary agriculture will face certain obstacles. As a result, a comprehensive review of various crop key factors such as climatic factors, soil nutrients, production factors, and environmental factors is conducted using a variety of machine learning approaches such as Support Vector Machine, bayes classifier, decision tree, random forest, linear regression and Extreme Learning Machines. The accuracy measures such as root mean square error, coefficient of determination and mean absolute error are used for comparing the performance of the system. Based on the findings of the reviews, an intelligent and robust machine learning technique provides the optimum option for achieving (i) soil fertility, (ii) crop prediction, and (iii) yield prediction. The importance of soil variables and the amount of nutrients available in the soil for growing crops has been found, according to an examination of 51 peer-reviewed studies, to create qualitative yield prediction. Furthermore, the investigations will yield recommendations for future fertilizer research.

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“…By measuring slope and regression coefficients, it can be represented in the form of mathematical equations in multiple linear regression. Using the regression coefficient formula, the intensity and direction of the relationship between the two variables can be calculated [19]. Hence when comparing with simple linear regression, MLR perform better with less error rate.…”

Section: A Multiple Linear Regressionmentioning

confidence: 99%

“…Moreover, multiple regression can be implemented in linear and non-linear modelling. Multiple regression is based on the statement that there is a linear relationship between both dependent and independent variables, where no assumption was made for major correlation between the independent variables [18][19][20].…”

Section: A Multiple Linear Regressionmentioning

confidence: 99%

Prediction of Tourist Visit in Taman Negara Pahang, Malaysia using Regression Models

Mutalib¹,

Razali²,

Kamarudin³

et al. 2021

IJACSA

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Tourism is among the significant source of income to Malaysia and Taman Negara Pahang is one of the Malaysia's tourism spots and the heritage of Malaysia in achieving the Sustainable Development Goals (SDG). It has attracted many international and local tourists for its richness in flora and fauna. Currently, the information of tourists' visits is not properly analyzed. This study integrates the internal and public information to analyze the visits. The regression models used are multiple linear regression, support vector regression, and decision tree regression to predict the tourism demand for Taman Negara, Malaysia and the best model was deployed. Predictive analytics can support the decision-making process for tourism destinations management. When the management gets a head-up of the demand in the future, they can choose a strategic planning and be more aware about the factors influencing tourism demand, such as the tourists' web search engine behaviors for accommodation, facilities, and attractions. The factors affecting the tourism demand are determined as the first objective. The role of independent variable was set to the total number of visitors, subsequently being set as the target variable in the modeling process. A total of 30 models were generated by tuning the cross-validation parameters. This study concluded that the best model is the multiple linear regression due to lower root mean square error (RSME) value.

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Prediction of Climate Variable using Multiple Linear Regression

Cited by 30 publications

References 6 publications

A Review on Linear Regression Comprehensive in Machine Learning

A Review on Linear Regression Comprehensive in Machine Learning

A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

Prediction of Tourist Visit in Taman Negara Pahang, Malaysia using Regression Models

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