Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia

Groundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously record...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ain Shams Engineering Journal Jg. 12; H. 2; S. 1545 - 1556
Hauptverfasser: Ibrahem Ahmed Osman, Ahmedbahaaaldin, Najah Ahmed, Ali, Chow, Ming Fai, Feng Huang, Yuk, El-Shafie, Ahmed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2021
Elsevier
Schlagworte:
Artificial neural network
Cross-correlation
Groundwater level prediction
Machine learning algorithm
Support vector regression
Support vector regression
Groundwater level prediction
Artificial neural network
Cross-correlation
Machine learning algorithm
ISSN:2090-4479
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Groundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously recorded data of rainfall, temperature and evaporation to predict groundwater levels. Three machine learning models have been tested and evaluated; Xgboost, Artificial Neural Network, and Support Vector Regression. The results showed that for the first scenario, which had combinations of 1,2 and 3 days delayed of rainfall data only considered as an input, the models’ performance was the worst. while in the second scenario the proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations. A significant increase in performance was achieved in the third scenario, when using 1 day delayed of groundwater levels as an input as well where R2 equal to 0.92 in the Xgboost model in scenario 3 and 0.16, 0.11 in scenarios 2 and 1 respectively. The results obtained in this study serves as a great benchmark for future groundwater levels prediction using Xgboost algorithm.
ISSN:2090-4479
DOI:10.1016/j.asej.2020.11.011