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Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies.

Wenjia Liu¹, Jingwen Chen¹, Haobo Wang¹

¹Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.

Environmental Science & Technology

|September 3, 2024

Summary

Multimodal learning (MML) offers advanced solutions for complex environmental issues by integrating diverse data. This approach enhances prediction models in environmental science and engineering (ES&E) for better environmental quality assessment and pollution control.

Keywords:

Computational Toxicology Deep Learning Environmental Modeling Environmental Science and Engineering Multimodal Learning

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Area of Science:

Environmental Science and Engineering (ES&E)
Multimodal Learning (MML)
Artificial Intelligence in Environmental Studies

Background:

Increasing anthropogenic impacts lead to complex environmental issues, often involving diverse data modalities.
Current machine learning (ML) models in ES&E frequently overlook the potential of multimodal data.
Environmental challenges threaten natural capital crucial for human well-being.

Purpose of the Study:

To explore the application of multimodal learning (MML) in environmental science and engineering (ES&E).
To summarize MML methodologies and their potential benefits for environmental modeling.
To identify challenges and future research directions for MML in ES&E.

Main Methods:

Review and summarization of existing multimodal learning (MML) methodologies.
Identification of potential applications of MML in environmental science and engineering (ES&E).
Discussion of implementation challenges and future research avenues.

Main Results:

Multimodal learning (MML) can provide more comprehensive descriptions of environmental issues by integrating diverse data.
MML has the potential to significantly improve the accuracy and robustness of prediction models in ES&E.
Proposed applications include environmental quality assessment, chemical hazard prediction, and pollution control optimization.

Conclusions:

Multimodal learning (MML) presents a promising approach to address complex environmental challenges.
Harnessing diverse data modalities through MML can lead to enhanced environmental modeling and solutions.
Further research is needed to overcome implementation challenges and fully realize MML's potential in ES&E.