Modeling Precipitation Patterns in Alappuzha -Kerala: An Analysis of Regression and Time Series Approaches

P, Isaac Ritharson.; Anitha, J.; Brema, J.; Juliet, D. Sujitha; S, Srinithisathian

doi:10.1109/conit59222.2023.10205530

Cited by 1 publication

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“…are identified as bringers of precipitation. These parameters are common in another study conducted in Kerala as well [12].…”

Section: Introductionmentioning

confidence: 91%

Assessing the Best Regression Approach for precipitation analysis with meteorological Data in Lucknow using PyCaret

2023

IRJMETS

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Accurate precipitation forecasting is of paramount importance in various fields, including agriculture, hydrology, environmental engineering and disaster management. This study investigates the suitability and performance of different regression models in predicting precipitation based on meteorological variables. Seven meteorological factors, including mean PBLH (Planetary Boundary Layer Height), wind speed, humidity, evapo-transpiration,, NDVI (Normalized Difference Vegetation Index), surface radiation and temperature, are considered as potential predictors of rainfall in this study.The objective is to identify the most effective regression model for quantifying the dependency of precipitation on these meteorological parameters. To achieve this, a comprehensive comparative analysis of regression models available in the PyCaret framework is conducted. Models such as Linear Regression, Lasso Regression, Random Forest Regression, K-Neighbors Regression and XGradient Boosting Regression, among others, are evaluated and compared. The best model is chosen based on certain parameters. The assessment includes metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared (R2) and Root Mean Squared Error (RMSE) to measure predictive accuracy. Additionally, residual analysis, learning curves, and error plots are employed to gain insights into model behaviour.Results from this study offer valuable guidance for selecting the most appropriate regression model for precipitation forecasting in different scenarios. Understanding the strengths and weaknesses of various models can lead to improved accuracy in predicting this critical meteorological variable, ultimately benefiting applications ranging from agriculture to disaster preparedness.

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“…are identified as bringers of precipitation. These parameters are common in another study conducted in Kerala as well [12].…”

Section: Introductionmentioning

confidence: 91%

Assessing the Best Regression Approach for precipitation analysis with meteorological Data in Lucknow using PyCaret

2023

IRJMETS

View full text Add to dashboard Cite

show abstract

Modeling Precipitation Patterns in Alappuzha -Kerala: An Analysis of Regression and Time Series Approaches

Cited by 1 publication

References 12 publications

Assessing the Best Regression Approach for precipitation analysis with meteorological Data in Lucknow using PyCaret

Assessing the Best Regression Approach for precipitation analysis with meteorological Data in Lucknow using PyCaret

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