Prospects,challenges and guidelines for practical deep learning in geoengineering  

作　　者：Guangqi Chen 

机构地区：[1]Kyushu University,Fukuoka 8190395,Japan [2]School of Civil and Transportation Engineering,Hebei University of Technology,Tianjin 300401,China

出　　处：《Intelligent Geoengineering》2024年第1期19-29,共11页智能岩土工程(英文)

基　　金：supported by National High-level Innovative Talents Scientific Research Project in Hebei Province,China(Grant No.405492).

摘　　要：Deep learning,a pivotal technology within artificial intelligence,has made significant strides across various domains,including geoengineering.This paper explores the practical applications and challenges of integrating deep learning techniques,such as Fully Connected Neural Networks(FCNNs)and Convolutional Neural Networks(CNNs),into geoengineering tasks,particularly in disaster prediction,resource exploration,and infrastructure health monitoring.The complexities of applying deep learning in geoengineering are multifaceted,involving mathematical,computational,and data processing challenges.However,the emergence of deep learning libraries,notably TensorFlow,has substantially lowered the technical barriers,enabling researchers and engineers to deploy these technologies more efficiently.Through case studies and practical examples,this paper demonstrates how TensorFlow can streamline the model development process,making deep learning more accessible to a broader audience in the field of geoengineering.The paper concludes with a discussion on the future prospects and potential advancements in the integration of deep learning within geoengineering,highlighting both the opportunities and the ongoing challenges.

关 键 词：Deep learning GEOENGINEERING FCNN CNN Practical application PROSPECTS DISASTER 

分 类 号：H31[语言文字—英语]