Estimation of soil moisture dynamics using a recurrent dynamic learning neural network

Tzeng, Y.C.; Fan, Kuo-Tai; Lin, Chih-Hong; Lee, Y. J.; Chen, K. S.

doi:10.1109/igarss.2012.6351314

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…In [58], a NARXNN model was used (called DLNN) to predict soil moisture on an hourly basis. The predictions were compared with ground measurements.…”

Section: Narxnn Applicationsmentioning

confidence: 99%

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Lutosławski

Hernes²,

Radomska

et al. 2021

IEEE Access

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Food demand prediction is a significant issue for both businesses processes improvement and sustainable development issues. The data science methods, including artificial intelligence methods, are often used for this purpose. The aim of this research is to develop the models for food demand prediction based on the Nonlinear Autoregressive Exogenous Neural Network. The research focuses on the processed food, such as bread or butter. Developed models' architectures differing in the number of hidden layers and the number of neurons in the hidden layers, as well as with different sizes of the delay-line, were tested for a given product. Results of the research show that depending on the type of product, prediction performance slightly differed. The results of the R 2 measure ranged from 96,2399 to 99,6477 depending on particular products. The proposed models can be used in a company's intelligent management system for rational control of inventories and food production. It can also lead to reducing food waste.INDEX TERMS food industry, sustainable development, neural networks, machine learning, demand forecasting.

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“…In [58], a NARXNN model was used (called DLNN) to predict soil moisture on an hourly basis. The predictions were compared with ground measurements.…”

Section: Narxnn Applicationsmentioning

confidence: 99%

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Lutosławski

Hernes²,

Radomska

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The direct validation indicated that their model remained stable and competitive in both frozen and unfrozen seasons. In addition, Tzeng et al [69] used a typical NARX model to estimate the dynamics of soil moisture. They used a NARX model (DLNN as they called) to predict soil moisture on an hourly basis and compared the predictions with ground measurements.…”

Section: Rnn Applications In Smart Agriculturementioning

confidence: 99%

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Zhu¹,

Liu²,

Liu³

et al. 2018

International Journal of Agricultural and Biological Engineering

139

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In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.

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Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics

Shi

Tso

et al. 2020

Journal of Hydrology

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Estimation of soil moisture dynamics using a recurrent dynamic learning neural network

Abstract: THE PROPOSED APPROACH

Cited by 5 publications

References 10 publications

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics

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