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

Ahmedbahaaaldin Ibrahem Ahmed Osman, Ali Najah Ahmed, Ming Fai Chow, Yuk Feng Huang, Ahmed El-Shafie

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1545-1556
Number of pages12
JournalAin Shams Engineering Journal
Volume12
Issue number2
DOIs
Publication statusPublished - Jun 2021
Externally publishedYes

Keywords

  • Artificial neural network
  • Cross-correlation
  • Groundwater level prediction
  • Machine learning algorithm
  • Support vector regression

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