An approach of recursive timing deep belief network for algal bloom forecasting

Wang, Li; Zhang, Tianrui; Jin, Xin; Xu, Jiping; Wang, Xiaoyi; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Zhao, Zhiyao; Xie, Yi

doi:10.1007/s00521-018-3790-9

Cited by 18 publications

(10 citation statements)

References 11 publications

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“…Unlike traditional prediction methods, machine learning methods do not require prior physical information, meaning prediction models can be built based on learning algorithms and historical data. Prediction is obtained based on mathematical models, and some parameter identification methods can be used, such as iterative algorithms [30][31][32], particle-based algorithms [33,34], and recursive and learning algorithms [35][36][37][38][39].…”

Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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“…Unlike the traditional models in Section 3 , the neural network model based on machine learning does not require other prior physical information or model assumptions [ 102 , 103 ]. In the model training stage, the model is obtained by learning the data’s hidden relationships and knowledge.…”

Section: Data-driven Modeling By Learningmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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“…The data can be more direct for the decision-making calculation. Moreover, the prediction of the water quality [39] should be introduced to pre-judge the trend. The prediction models [40][41][42][43] and data estimation methods [44,45] can help data analysis in the aforehand decision-making.…”

Section: Extension and Improvement Of Group Decision-makingmentioning

confidence: 99%

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

et al. 2020

Self Cite

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Algal bloom is a typical pollution of urban lakes, which threatens drinking safety and breaks the urban landscape. It is pivotal to select a reasonable governance approach for sustainable management. A decision-making support method was studied in this paper. First, a general framework was designed to organize the rational decision-making processes. Second, quantitative calculation methods were proposed, including expert selection and opinion integration. The methods can determine the vital decision elements objectively and automatically. Third, the method was applied in Yuyuantan Lake in Beijing, China. The monitoring information and decision-making process are presented and the rank of governance alternatives is given. The comparison and discussion show that the group decision-making method is feasible and effective. It can assist the sustainable management of algal bloom.

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An approach of recursive timing deep belief network for algal bloom forecasting

Cited by 18 publications

References 11 publications

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

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