Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

Ouedraogo, Wend Yam Serge Boris; Tiemounou, Sibiri; Djibo, Moumouni; Doumounia, Ali; Sanou, Serge Roland; Sawadogo, Moumouni; Guira, Idrissa; Zougmoré, François

doi:10.4236/eng.2022.142008

“…These studies include some of the most popular statistical models, such as linear regression (LR), poison regression (PR), and evolutionary polynomial regression (EPR). As machine-learning techniques, they use gradient boost trees (GB) [4][5][6][7] , Bayesian belief networks 8-10 , Support Vector Machines (SVMs) [11][12][13] and Arti cial Neural Networks (ANNs) 11,[14][15][16][17][18][19] . These studies have consistently found that ML models can provide valuable insights into the condition of these pipelines and help prioritize maintenance and repair efforts based on forecasting the failure rate of water pipes; however, ensemble approaches for water pipe leakage predictions have yet to be thoroughly investigated.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Model for Forecasting Water-pipe Leakage

Warad,

Wassif,

Darwish

2024

Preprint

0

View full text Add to dashboard Cite

Based on the benefits of different ensemble methods, such as bagging and boosting, which have been studied and adopted extensively in research and practice, where bagging and boosting focus more on reducing variance and bias, this paper presented an optimization ensemble learning-based model for a large pipe failure dataset of water pipe leakage forecasting, something that was not previously considered by others. It is known that tuning the hyperparameters of each base learned inside the ensemble weight optimization process can produce better-performing ensembles, so it effectively improves the accuracy of water pipe leakage forecasting based on the pipeline failure rate. To evaluate the proposed model, the results are compared with the results of the bagging ensemble and boosting ensemble models using the root-mean-square error (RMSE), the mean square error (MSE), the mean absolute error (MAE), and the coefficient of determination (R2) of the bagging ensemble technique, the boosting ensemble technique and optimizable ensemble technique are higher than other models. The experimental result shows that the optimizable ensemble model has better prediction accuracy. The optimizable ensemble model has achieved the best prediction of water pipe failure rate at the 14th iteration, with the least RMSE = 0.00231 and MAE = 0.00071513 when building the model that predicts water pipe leakage forecasting via pipeline failure rate.

show abstract

A review of dust-induced electromagnetic waves scattering theories and models for 5G and beyond wireless communication systems

Alozie

¹

,

Musa

²

,

Фарук

³

et al. 2023

Scientific African

4

0

View full text Add to dashboard Cite

An ensemble learning model for forecasting water-pipe leakage

Warad,

Wassif,

Darwish

2024

Sci Rep

2

0

View full text Add to dashboard Cite

Based on the benefits of different ensemble methods, such as bagging and boosting, which have been studied and adopted extensively in research and practice, where bagging and boosting focus more on reducing variance and bias, this paper presented an optimization ensemble learning-based model for a large pipe failure dataset of water pipe leakage forecasting, something that was not previously considered by others. It is known that tuning the hyperparameters of each base learned inside the ensemble weight optimization process can produce better-performing ensembles, so it effectively improves the accuracy of water pipe leakage forecasting based on the pipeline failure rate. To evaluate the proposed model, the results are compared with the results of the bagging ensemble and boosting ensemble models using the root-mean-square error (RMSE), the mean square error (MSE), the mean absolute error (MAE), and the coefficient of determination (R2) of the bagging ensemble technique, the boosting ensemble technique and optimizable ensemble technique are higher than other models. The experimental result shows that the optimizable ensemble model has better prediction accuracy. The optimizable ensemble model has achieved the best prediction of water pipe failure rate at the 14th iteration, with the least RMSE = 0.00231 and MAE = 0.00071513 when building the model that predicts water pipe leakage forecasting via pipeline failure rate.

show abstract

Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

Cited by 3 publications

References 13 publications

An Ensemble Learning Model for Forecasting Water-pipe Leakage

An Ensemble Learning Model for Forecasting Water-pipe Leakage

A review of dust-induced electromagnetic waves scattering theories and models for 5G and beyond wireless communication systems

An ensemble learning model for forecasting water-pipe leakage

Contact Info

Product

Resources

About