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The weighted multi-scale connections networks for macrodispersivity estimation.

Zhengkun Zhou¹, Kai Ji¹

¹College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.

Journal of Contaminant Hydrology

|July 14, 2024

Summary

A new method, weighted multi-scale connections (WMC), enhances convolutional neural networks (CNNs) for estimating macrodispersivity. This approach improves prediction accuracy in solute transport models by strengthening feature propagation and mitigating gradient issues.

Keywords:

Convolutional neural network Deep learning Groundwater Hydraulic conductivity field Macrodispersivity

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Last Updated: May 12, 2026

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Area of Science:

Environmental Science
Geosciences
Computational Science

Background:

Macrodispersivity is crucial for accurate solute transport modeling.
Traditional estimation methods are computationally intensive.
Convolutional neural networks (CNNs) show promise but require accuracy improvements.

Purpose of the Study:

Introduce weighted multi-scale connections (WMC) to enhance CNNs for macrodispersivity estimation.
Improve the accuracy and efficiency of predicting solute transport.
Address limitations in current CNN-based approaches.

Main Methods:

Developed and implemented weighted multi-scale connections (WMC) within various CNN architectures (CNN, ResNet, DenseNet).
Evaluated WMC performance on macrodispersivity estimation datasets.
Compared WMC-enhanced CNNs against baseline models.

Main Results:

WMC significantly improved R-squared values across all tested CNN architectures.
Improvements ranged from 3% for basic CNNs to over 30% for deeper ResNets.
WMC effectively strengthens feature propagation and alleviates vanishing-gradient problems.

Conclusions:

Weighted multi-scale connections offer a robust method for enhancing CNN performance in macrodispersivity estimation.
The WMC technique is versatile and applicable to various CNN architectures with down-sampling layers.
This advancement offers more accurate and efficient predictions for solute transport modeling.