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An optimization-driven hierarchical deep learning approach using the Gray Langurs algorithm for data-driven seismic

Mahmoud Shabrawy¹, El-Sayed M El-Kenawy², Nahla B Abdel-Hamid³

¹Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. mshabrawy@std.mans.edu.eg.

Scientific Reports

|June 16, 2026

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This summary is machine-generated.

This study introduces a new deep learning framework for predicting seismic activity using historical earthquake data. The enhanced model significantly improves prediction accuracy and stability by integrating hierarchical forecasting with metaheuristic optimization.

Area of Science:

Geophysics
Data Science
Computational Seismology

Background:

Seismic time series data present challenges due to non-stationarity, multi-scale properties, and clustering.
Existing data-driven seismic prediction models often lack systematic hyperparameter optimization, limiting performance.
There is a need for integrated, computationally efficient frameworks for data-driven seismic time-series modeling.

Purpose of the Study:

To develop and evaluate a hierarchical deep learning-metaheuristic optimization framework for seismic activity prediction.
To benchmark the proposed framework against state-of-the-art deep time-series models.
To demonstrate the impact of adaptive hyperparameter optimization on prediction accuracy and stability.

Main Methods:

A novel framework combining the Neural Hierarchical Interpolation for Time Series Forecasting (N-HITS) algorithm with the Gray Langurs Optimizer (GLO) was proposed.

Keywords:

Data-driven seismic activity prediction Gray Langurs optimizer (GLO)Metaheuristic hyperparameter optimization Neural hierarchical interpolation (N-HITS)Seismic time-series trend modeling

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Systematic benchmarking of N-HITS against other deep time-series models under identical conditions was performed.

Adaptive hyperparameter optimization using GLO was applied to the N-HITS model.

Main Results:

N-HITS demonstrated strong baseline performance with R² of 0.921 and MSE of 0.00234.
GLO-based hyperparameter optimization significantly improved N-HITS performance, achieving an R² of [Formula: see text] and MSE of 7.980e-05 ± 7.980e-07.
The optimized model showed substantial error reduction and enhanced convergence stability.

Conclusions:

Optimization intelligence is crucial for improving catalog-based statistical seismic activity prediction.
The proposed hierarchical deep learning with adaptive metaheuristic search offers a scalable architecture for seismic trend monitoring.
The model provides statistical trend estimates based on historical data, not physically-based earthquake predictions.