An approach of improved Multivariate Timing-Random Deep Belief Net modelling for algal bloom prediction

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

doi:10.1016/j.biosystemseng.2018.09.005

Cited by 37 publications

(17 citation statements)

References 22 publications

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“…, Wang et al. ). However, these systems have generally focused on forecasting cell counts or biomass of target species, or total chlorophyll concentrations.…”

Section: Discussionmentioning

confidence: 99%

“…The application of deep learning helps to incorporate the high-variance information contained in local, weekly measurements, allowing the forecasts to operate at fine scales. Neural networks have been used already to some degree of success in this regard (Velo-Su arez and Guti errez-Estrada 2007, Kang et al 2011, Guallar et al 2016, Qin et al 2017, Wang et al 2019). However, these systems have generally focused on forecasting cell counts or biomass of target species, or total chlorophyll concentrations.…”

Section: Discussionmentioning

confidence: 99%

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The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine

et al. 2019

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Farmed and wild harvest shellfish industries are increasingly important components of coastal economies globally. Disruptions caused by harmful algal blooms (HABs), colloquially known as red tides, are likely to worsen with increasing aquaculture production, environmental pressures of coastal development, and climate change, necessitating improved HAB forecasts at finer spatial and temporal resolution. We leveraged a dataset of chemical analytical toxin measurements in coastal Maine to demonstrate a new machine learning approach for high‐resolution forecasting of paralytic shellfish toxin accumulation. The forecast used a deep learning neural network to provide weekly site‐specific forecasts of toxicity levels. The algorithm was trained on images constructed from a chemical fingerprint at each site composed of a series of toxic compound measurements. Under various forecasting configurations, the forecast had high accuracy, generally >95%, and successfully predicted the onset and end of nearly all closure‐level toxic events at the site scale at a one‐week forecast time. Tests of forecast range indicated a decline in accuracy at a three‐week forecast time. Results indicate that combining chemical analytical measurements with new machine learning tools is a promising way to provide reliable forecasts at the spatial and temporal scales useful for management and industry.

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“…, Wang et al. ). However, these systems have generally focused on forecasting cell counts or biomass of target species, or total chlorophyll concentrations.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine

et al. 2019

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“…Any selected governance approach should be conducted according to sustainable principles, which requires more factors and limitations in management and decision making. With the development of information technology, more techniques can help with modern sustainable management, including machine learning [16][17][18], information fusion [19,20], sensor networks [21][22][23], and Internet of Things [24,25].…”

Section: Algal Bloom Governance and Sustainable Managementmentioning

confidence: 99%

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

et al. 2020

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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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“…Some methods have been proposed to solve the prediction problem for the collected time sequential data based on the sensors of the IoT system. For example, the traditional autoregressive integrated moving average (ARIMA) [10], artificial neural networks (ANN) [11][12][13][14], support vector machines (SVMs) [15], and echo state network (ESN) with particle swarm optimization [16] have been applied to the modeling and predicting of the future of time sequential data. However, for the practical IoT system, these models cannot obtain accurate predictions due to the complexity of the collected data and weak modeling ability for nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Jin

Wang

et al. 2020

Sustainability

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Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

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An approach of improved Multivariate Timing-Random Deep Belief Net modelling for algal bloom prediction

Cited by 37 publications

References 22 publications

The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine

The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine

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

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

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