A hybrid CNN-GRU model for predicting soil moisture in maize root zone

Yu, Jingxin; Zhang, Xin; Xu, Linlin; Dong, Jiwen; Zhangzhong, Lili

doi:10.1016/j.agwat.2020.106649

Cited by 100 publications

(72 citation statements)

References 16 publications

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“…The smaller the metric value means the higher the forecast accuracy. In the literature, MSE, root means square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as forecasting performance evaluation metrics (KPIs) [ [82] , [83] , [84] , [85] ]. Different KPIs are used in this study to evaluate the accuracy of the proposed methodology from various perspectives [ 86 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Ayyıldız

Erdoğan

Gümüş

2021

Computers in Biology and Medicine

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Section: Proposed Methodologymentioning

confidence: 99%

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Ayyıldız

Erdoğan

Gümüş

2021

Computers in Biology and Medicine

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“…In this article, a hybrid model is proposed, which is a combination of GoogLeNet and GRU. In [30], [31], the authors prove that hybrid deep learning models perform better than individual learners. The proposed model takes advantages of both GoogLeNet and GRU by extracting and remembering This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: B Architecture Of Hybrid Modelmentioning

confidence: 99%

“…In [7], [31], the authors exploit 2D-CNN model to the extract abstract features from time series dataset. Motivated from these articles, the GoogLeNet is applied to extract latent features from EC data.…”

Section: Googlenetmentioning

confidence: 99%

“…Figure 6c shows loss and accuracy curves on training and testing datasets that are better than curves of GRU and GoogLeNet models, which are presented in Figures 4c and 5c. In [30] and [31], the authors prove that a hybrid deep learning model performs However, when we combine it with GoogLeNet model, then the overall performance is improved. The combined model has the ability to learn better patterns from EC data.…”

Section: Performance Analysis Of Hybrid Hg 2 Modelmentioning

confidence: 99%

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A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

et al. 2021

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For dealing with the electricity theft detection in the smart grids, this article introduces a hybrid deep learning model. The model tackles various issues such as class imbalance problem, curse of dimensionality and low theft detection rate of the existing models. The model integrates the benefits of both GoogLeNet and gated recurrent unit (GRU). The one dimensional electricity consumption (EC) data is fed into GRU to remember the periodic patterns of electricity consumption. Whereas, GoogLeNet model is leveraged to extract the latent features from the two dimensional weekly stacked EC data. Furthermore, the time least square generative adversarial network (TLSGAN) is proposed to solve the class imbalance problem. The TLSGAN uses unsupervised and supervised loss functions to generate fake theft samples, which have high resemblance with real world theft samples. The standard generative adversarial network only updates the weights of those points that are available at the wrong side of the decision boundary. Whereas, TLSGAN even modifies the weights of those points that are available at the correct side of decision boundary that prevent the model from vanishing gradient problem. Moreover, dropout and batch normalization layers are utilized to enhance model's convergence speed and generalization ability. The proposed model is compared with different state-of-the-art classifiers including multilayer perceptron (MLP), support vector machine, naive bayes, logistic regression, MLP-long short term memory network and wide and deep convolutional neural network. It outperforms all classifiers by achieving 96% and 97% precision-recall area under the curve and receiver operating characteristics area under the curve, respectively.INDEX TERMS Electricity theft detection, gated recurrent unit, GoogLeNet, non-technical losses, smart grids, SGCC.

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“…Yu et al [66] proposed a hybrid convolutional neural network-gated recurrent unit (CNN-GRU) for predicting soil moisture in maize root zone using input from soil moisture content and meteorological variables from five different cultivation areas. The validation results showed that CNN-RGU performs better than the CNN and GRU model alone.…”

Section: Soil Water Modellingmentioning

confidence: 99%

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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A hybrid CNN-GRU model for predicting soil moisture in maize root zone

Cited by 100 publications

References 16 publications

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

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