Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
- Title
- Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
- Creator
- George J.; Yadav J.; Nair A.M.; Peter M.V.; Alapatt B.P.; Baby R.
- Description
- In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE.
- Source
- 2024 3rd International Conference for Advancement in Technology, ICONAT 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- ARIMA-XGB; Decision Trees; Groundwater level prediction; LGBM; MAE; Random Forest; RMSE; Stacking and ensemble; XGB
- Coverage
- George J., CHRIST (Deemed to Be University), India; Yadav J., CHRIST (Deemed to Be University), India; Nair A.M., Luxsh Solutions Private Limited, India; Peter M.V., CHRIST (Deemed to Be University), India; Alapatt B.P., CHRIST (Deemed to Be University), India; Baby R., CHRIST (Deemed to Be University), India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035417-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
George J.; Yadav J.; Nair A.M.; Peter M.V.; Alapatt B.P.; Baby R., “Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/18993.