EGU24-14765, updated on 09 Mar 2024
https://doi.org/10.5194/egusphere-egu24-14765
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Optimizing Groundwater Forecasting: Comparative Analysis of MLP Models Using Global and Regional Precipitation Data

Akanksha Soni1, Surajit Deb Barma2, and Amai Mahesha2
Akanksha Soni et al.
  • 1Department of Civil Engineering, Indian Institute of Technology Madras, India (akankshac.e.07@gmail.com)
  • 2Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, India. (surajitdb@gmail.com)

This study investigates the efficacy of Multi-Layer Perceptron (MLP) models in groundwater level modeling, specifically emphasizing the pivotal role of input data quality, particularly precipitation data. Unlike prior research that primarily focused on regional datasets like those from the India Meteorological Department (IMD), our research explores the integration of global precipitation data, specifically leveraging the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset for MLP-based modeling. The assessment was conducted using two wells in Dakshina Kannada, evaluating four MLP models (GA-MLP, EFO-MLP, PSO-MLP, AAEO-MLP) with IMERG and IMD precipitation data. Performance metrics were employed, including mean absolute error, root mean square error, normalized Nash-Sutcliffe efficiency, and Pearson's correlation index. The study also includes convergence analysis and stability assessments, revealing the significant impact of the precipitation dataset on model performance. Noteworthy findings include the superior performance of the AAEO-MLP model in training with IMD data and the GA-MLP model's outperformance in testing at the Bajpe well with both datasets. The stability of the GA-MLP model, indicated by the lowest standard deviation values in convergence analysis, underscores its reliability. Moreover, transitioning to the IMERG dataset improved model performance and reduced variability, providing valuable insights into the strengths and limitations of MLP models in groundwater-level modeling. These results advance the precision and dependability of groundwater level forecasts, thereby supporting more effective strategies for international groundwater resource management.

How to cite: Soni, A., Barma, S. D., and Mahesha, A.: Optimizing Groundwater Forecasting: Comparative Analysis of MLP Models Using Global and Regional Precipitation Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14765, https://doi.org/10.5194/egusphere-egu24-14765, 2024.

Supplementary materials

Supplementary material file

Comments on the supplementary material

AC: Author Comment | CC: Community Comment | Report abuse

supplementary materials version 1 – uploaded on 18 Apr 2024, no comments