Bayesian Multi-modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction
Bayesian Multi-modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction
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DOI：
10.1016/j.agrformet.2021.108773
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发表时间：
2022-03-01
影响因子：
6.2
通讯作者：
Moradkhani, Hamid
中科院分区：
文献类型：
--
作者：
Abbaszadeh, Peyman;Gavahi, Keyhan;Moradkhani, Hamid
农业规划的一个必要方面是准确的产量预测。人工智能（AI）技术，如深度学习（DL），已被认为是实现这一问题的实际解决方案的有效手段。然而，这些方法通常提供确定性的估计，并没有考虑到模型预测中涉及的不确定性。该研究提出了一个框架，该框架采用贝叶斯模型平均（BMA）和一组Copula函数来整合多个深度神经网络的输出，包括3DCNN（3D卷积神经网络）和ConvLSTM（卷积长短期记忆），并提供了美国三个州100个县的大豆作物产量的概率估计。这项研究的结果表明，所提出的方法比单独使用3DCNN和ConvLSTM网络更准确可靠地预测大豆作物产量，同时考虑了模型的不确定性。
An imperative aspect of agricultural planning is accurate yield prediction. Artificial Intelligence (AI) techniques, such as Deep Learning (DL), have been recognized as effective means for achieving practical solutions to this problem. However, these approaches most often provide deterministic estimates and do not account for the uncertainties involved in model predictions. This study presents a framework that employs the Bayesian Model Averaging (BMA) and a set of Copula functions to integrate the outputs of multiple deep neural networks, including the 3DCNN (3D Convolutional Neural Network) and ConvLSTM (Convolutional Long Short-Term Memory), and provides a probabilistic estimate of soybean crop yield over a hundred counties across three states in the United States. The results of this study show that the proposed approach produces more accurate and reliable soybean crop yield predictions than the 3DCNN and ConvLSTM networks alone while accounting for the models' uncertainties.