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Updated: Jun 26, 2025

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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级联相关神经网络 (CCNN) 和前神经网络 (FFNN) 之间的比较调查,用于预测悬浮沉积物度的机器学习模型

Bhupendra Joshi1, Vijay Kumar Singh2, Dinesh Kumar Vishwakarma3

  • 1Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India.

Scientific reports
|May 9, 2024
PubMed
概括

级联相关神经网络 (CCNN) 模型在预测每日悬浮沉积物度 (SSC) 方面表现优于前神经网络 (FFNN). CCNN展示了在印度Sheonath盆地SSC的优越水文预测潜力.

关键词:
级联相关神经网络的神经网络.在Feedforward神经网络中,Feedforward是一个神经网络.机器学习 机器学习塞奥纳特盆地是西奥纳特的盆地.悬浮沉积物度的悬浮沉积物度是多少

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