Design of Deep Belief Networks for Short‐Term Prediction of Drought Index Using Data in the Huaihe River Basin

Chen, Junfei; Jin, Qiongji; Chao, Jinhui

doi:10.1155/2012/235929

Cited by 51 publications

(23 citation statements)

References 42 publications

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“…3D (depth and shape) analysis [107][108][109][110][111][112][113][114][115] Advanced driver assistance systems [116][117][118][119][120] Animal detection 121 Anomaly detection 122 Automated Target Recognition [123][124][125][126][127][128][129][130][131][132][133][134] Change detection [135][136][137][138][139] Classification Data fusion 191 Dimensionality reduction 192,193 Disaster analysis/assessment 194 Environment and water analysis [195][196][197][198] Geo-information extraction 199 Human detection [200][201][202][203] Image denoising/enhancement 204,…”

Section: References Area Referencesmentioning

confidence: 99%

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball

Anderson

Chan

2017

J. Appl. Remote Sens

538

331

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Abstract. In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: References Area Referencesmentioning

confidence: 99%

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball

Anderson

Chan

2017

J. Appl. Remote Sens

538

331

View full text Add to dashboard Cite

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“…at is, process data are time series with strong nonlinearities and dynamics, which increases the difficulty of modelling with conventional NNs. Recently, a powerful type of NN named long short-term memory (LSTM) was designed to handle sequence dependence [13][14][15]. An LSTM network is more significant in learning long-term temporal dependencies since its memory cells can maintain its state over a long time and standardize the information moving into and out of the cell.…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Soft Sensor with Long Short-Term Memory Network and Normalized Mutual Information Feature Selection

Li¹,

Sun

2020

Mathematical Problems in Engineering

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In this paper, a novel soft sensor is developed by combining long short-term memory (LSTM) network with normalized mutual information feature selection (NMIFS). In the proposed algorithm, LSTM is designed to handle time series with high nonlinearity and dynamics of industrial processes. NMIFS is conducted to perform the input variable selection for LSTM to simplify the excessive complexity of the model. The developed soft sensor combines the excellent dynamic modelling of LSTM and precise variable selection of NMIFS. Simulations on two actual production datasets are used to demonstrate the performance of the proposed algorithm. The developed soft sensor could precisely predict the objective variables and has better performance than other methods.

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“…As a typical deep learning approach, deep belief networks (DBN) have attracted wide spread attention and has gained some achievement in the fields of pattern recognition (Nie et al, 2015;Liu et al, 2016) and complex phenomena prediction (Cheng et al, 2017). Some studies also demonstrated that DBN-based drought prediction model (Chen et al, 2012) and algal bloom forecast model (Zhang et al, 2016) have an advantage over the shallow learning approach in respect of generalisation and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning based approach for forecast of water quality in intensive shrimp culture ponds

Qi¹,

Yang²,

Cheng³

et al. 2018

Indian J. Fish.

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With the enormous development of aquaculture, reducing the impacts of effluent discharge and improving water quality had become a critical global environmental concern. It is important to assess and predict water quality in the environmental management process of shrimp mariculture. Meanwhile, the accurate forecast of water quality is still in the exploration stage at present. In this study, deep belief networks (DBN) model are used to forecast water quality in intensive shrimp culture. This method based on deep learning includes a five-layered structure to extract relationships between the quantitative characteristic of water bodies and water quality variables. The water quality can be forecasted by the Canadian Water Quality Index (WQI) obtained from the output layer of simulated model. The results show that the DBN model has a great potential to predict the water quality and the ability of generalization and accuracy of model are satisfied.

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Design of Deep Belief Networks for Short‐Term Prediction of Drought Index Using Data in the Huaihe River Basin

Cited by 51 publications

References 42 publications

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Development of a Novel Soft Sensor with Long Short-Term Memory Network and Normalized Mutual Information Feature Selection

Deep-learning based approach for forecast of water quality in intensive shrimp culture ponds

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