机构名称：
¥ 3.0
1 IIHR—Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA 2 Civil and Environmental Engineering, University of Iowa, Iowa City, Iowa, USA 3 Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA * Corresponding Author: bekirzahit-demiray@uiowa.edu Abstract Harmful algal blooms (HABs) have由于人类活动和气候变化的综合作用，影响了水生生态系统，饮用水供应系统和人类健康，因此成为了重大的环境挑战。This study investigates the performance of deep learning models, particularly the Transformer model, as there are limited studies exploring its effectiveness in HAB prediction, considering multiple influencing parameters including physical, chemical, and biological water quality monitoring data from multiple stations located west of Lake Erie, and uses Shapley Additive Explanations (SHAP) values as an explainable artificial intelligence (AI) tool to identify key input features affecting HABs.我们的发现突出了深度学习模型，尤其是变压器的优越性，捕获了水质参数的复杂动态，并为生态管理提供了可行的见解。SHAP分析将颗粒有机碳，颗粒有机氮和总磷视为影响HAB预测的关键因素。这项研究有助于开发HAB的先进预测模型，这有助于早期检测和主动管理策略。关键字：有害藻华（HAB），预测，深度学习，变压器，叶绿素-a，水质，可解释的AI，形状值。此手稿是一个地球预印本，已在同行评审期刊中提交了可能的出版物。请注意，此前尚未进行同行评审，目前正在首次接受同行评审。此手稿的后续版本可能具有略有不同的内容。
使用可解释的深度学习模型预测有害的藻华：
主要关键词
相关文件推荐
