Exploring Use of Machine Learning Regressors for Daily Rainfall Prediction in the Sahel Region: A Case Study of Matam, Senegal

Nyasulu, Chimango; Diattara, Awa; Traore, Assitan; Dème, Abdoulaye; Ba, Cheikh Oumar

doi:10.1007/978-3-031-25271-6_5

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By conducting a comparative analysis of regression models, which have worked well in predicting rainfall in a study in Senegal [14] using the PyCaret framework, we seek to contribute valuable insights into the selection of appropriate models for precipitation forecasting. The findings of this study are expected to enhance our understanding of the relationship between meteorological variables and precipitation in Lucknow and have broader applications in monsoonal regions across India.…”

Section: Introductionmentioning

confidence: 99%

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

2023

IRJMETS

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities

Sharifi,

Tarlani Beris,

Sharifzadeh Javidi

et al. 2024

Advanced Engineering Informatics

View full text Add to dashboard Cite

Exploring Use of Machine Learning Regressors for Daily Rainfall Prediction in the Sahel Region: A Case Study of Matam, Senegal

Cited by 2 publications

References 8 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

Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities

Contact Info

Product

Resources

About