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Wu Y, Sun L, Sun X, Wang B. A hybrid XGBoost-ISSA-LSTM model for accurate short-term and long-term dissolved oxygen prediction in ponds. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:18142-18159. [PMID: 34686955 DOI: 10.1007/s11356-021-17020-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/09/2021] [Indexed: 06/13/2023]
Abstract
Dissolved oxygen (DO) is one of the most critical factors to measure the water quality in ponds, which greatly impacts on healthy growth of aquatic organisms. To improve the prediction accuracy of DO and grasp its changing trends, a novel hybrid DO prediction model based on the long short-term memory network (LSTM) optimized by an improved sparrow search algorithm (ISSA) is proposed. Firstly, to discard redundant information and improve the calculation speed of the model, the key factors that have a greater correlation with DO are selected as the input parameters by extreme gradient boosting (XGBoost). Secondly, towards expanding the searching range of sparrows and balancing the global and local search, we introduce an adaptive factor exponential declining strategy for producers, and an arcsine decreasing strategy for scouters, which nonlinearly decreases with the increase of iterations. Besides, we also improve the position updating of scouters, making the sparrows gradually move to the best position. Finally, LSTM is optimized by ISSA to get the best initial weights and thresholds to construct an XGBoost-ISSA-LSTM DO prediction model. Specifically, we first analyze the method for water quality prediction, which can make short-term prediction (including about 1 h, 2 h) and long-term prediction (including about 12 h, 24 h) of DO. In 1-h prediction, the root mean square error (RMSE) of the model is 0.5571, the mean absolute error (MAE) is 0.2572, and the R2 is 0.9276. In 24 h prediction, RMSE of the model is 0.6310, MAE is 0.4562, and R2 is 0.9082. The experimental results show that the proposed model has better generalization performance and higher prediction accuracy compared with other common models. Therefore, the presented model based on XGBoost-ISSA-LSTM is more effective and could meet the actual demand of accurate prediction of DO.
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Affiliation(s)
- Yuhan Wu
- National Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, P. O. Box 121, Beijing, 100083, People's Republic of China
- Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Longqing Sun
- National Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, P. O. Box 121, Beijing, 100083, People's Republic of China.
- Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
| | - Xibei Sun
- National Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, P. O. Box 121, Beijing, 100083, People's Republic of China
- Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Boning Wang
- National Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, P. O. Box 121, Beijing, 100083, People's Republic of China
- Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
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2
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Sarower MG, Mahmud-Al-Hasan M, Rahman MS, Hasan MM, Ahmmed MK, Ali MY, Giteru SG, Banu GR. Comparative growth and morphometric assessment between cultures of wild and hatchery-produced mud crabs. Heliyon 2021; 7:e07964. [PMID: 34557604 PMCID: PMC8445846 DOI: 10.1016/j.heliyon.2021.e07964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 07/16/2021] [Accepted: 09/07/2021] [Indexed: 12/01/2022] Open
Abstract
This paper reports the comparative growth, nutritional performance, and morphometric variation between wild and hatchery-reared juvenile mud crabs (Scylla olivacea) in earthen ponds. The crabs were fed daily with boiled tilapia paste at a feeding rate of 5–8% body weight for the first two weeks, followed by feeding with chopped eviscerated tilapia until termination of the experiment. Selected phenotypic trains, including carapace width (CW), carapace length (CL) and abdominal width (AW), were measured weekly. The protein content of the muscle (21.13%), gill (13.51%) and egg (43.28%) were significantly higher in the hatchery-sourced compared to wild female crabs (muscle = 19.15%; gill = 10.09%; egg = 38.15%). Likewise, the hatchery sourced crabs exhibited higher lipid content in the muscle (2.45–2.51%) and eggs (7.51%) compared to the wild counterparts (muscle = 1.45–1.47%; egg = 6.15%). These findings suggested a superior nutritional quality of the hatchery-reared compared to the wild-sourced crabs. Although some selected phenotypic traits did not vary among the wild and hatchery-reared crabs (p < 0.05), their survival rates varied significantly depending on the stocking density (p < 0.05). Overall, the findings suggest that the growth characteristics of the hatchery produced and wild-sourced crab were similar, which will help to remove the misconception among the crab farmers about the hatchery seeds and promote diversification of the crab production system for long-term sustainability.
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Affiliation(s)
- Md Golam Sarower
- Fisheries and Marine Resource Technology Discipline, Khulna University, Khulna 9208, Bangladesh
| | - Md Mahmud-Al-Hasan
- Fisheries and Marine Resource Technology Discipline, Khulna University, Khulna 9208, Bangladesh
| | - Md Shohanur Rahman
- Fisheries and Marine Resource Technology Discipline, Khulna University, Khulna 9208, Bangladesh
| | - Md Mehedi Hasan
- Fisheries and Marine Resource Technology Discipline, Khulna University, Khulna 9208, Bangladesh
| | - Mirja Kaizer Ahmmed
- Department of Food Science, University of Otago, Dunedin 9016, New Zealand.,Department of Fishing and Post-harvest Technology, Faculty of Fisheries, Chittagong Veterinary and Animal Sciences University, Bangladesh
| | - Muhammad Yousuf Ali
- Fisheries and Marine Resource Technology Discipline, Khulna University, Khulna 9208, Bangladesh
| | - Stephen G Giteru
- Food Chemistry and Structure Team, AgResearch Limited, Palmerston North, 4410, New Zealand
| | - Ghausiatur Reza Banu
- Fisheries and Marine Resource Technology Discipline, Khulna University, Khulna 9208, Bangladesh
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3
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Sun J, Li D, Fan D. A novel dissolved oxygen prediction model based on enhanced semi-naive Bayes for ocean ranches in northeast China. PeerJ Comput Sci 2021; 7:e591. [PMID: 34179455 PMCID: PMC8205303 DOI: 10.7717/peerj-cs.591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 05/21/2021] [Indexed: 06/13/2023]
Abstract
A challenge of achieving intelligent marine ranching is the prediction of dissolved oxygen (DO). DO directly reflects marine ranching environmental conditions. Through accurate DO predictions, timely human intervention can be made in marine pasture water environments to avoid problems such as reduced yields or marine crop death due to low oxygen concentrations in the water. We use an enhanced semi-naive Bayes model for prediction based on an analysis of DO data from marine pastures in northeastern China from the past three years. Based on the semi-naive Bayes model, this paper takes the possible values of a DO difference series as categories, counts the possible values of the first-order difference series and the difference series of the interval before each possible value, and selects the most probable difference series value at the next moment. The prediction accuracy is optimized by adjusting the attribute length and frequency threshold of the difference sequence. The enhanced semi-naive Bayes model is compared with LSTM, RBF, SVR and other models, and the error function and Willmott's index of agreement are used to evaluate the prediction accuracy. The experimental results show that the proposed model has high prediction accuracy for DO attributes in marine pastures.
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Affiliation(s)
- Jiajun Sun
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
- Key Laboratory of Intelligent Information Processing, Shandong Technology and Business University, Yantai, Shandong, China
- Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, Shandong, China
| | - Dashe Li
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
- Key Laboratory of Intelligent Information Processing, Shandong Technology and Business University, Yantai, Shandong, China
- Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, Shandong, China
| | - Deming Fan
- School of Computer Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong, China
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4
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AlDahoul N, Essam Y, Kumar P, Ahmed AN, Sherif M, Sefelnasr A, Elshafie A. Suspended sediment load prediction using long short-term memory neural network. Sci Rep 2021; 11:7826. [PMID: 33837236 PMCID: PMC8035216 DOI: 10.1038/s41598-021-87415-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/26/2021] [Indexed: 11/09/2022] Open
Abstract
Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.
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Affiliation(s)
- Nouar AlDahoul
- Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Malaysia
| | - Yusuf Essam
- Institute of Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Pavitra Kumar
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (U.M.), 50603, Kuala Lumpur, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Mohsen Sherif
- National Water and Energy Center, United Arab Emirates University, P.O.Box: 15551, Al Ain, United Arab Emirates. .,Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, P.O.Box:15551,, Al Ain, United Arab Emirates.
| | - Ahmed Sefelnasr
- National Water and Energy Center, United Arab Emirates University, P.O.Box: 15551, Al Ain, United Arab Emirates
| | - Ahmed Elshafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (U.M.), 50603, Kuala Lumpur, Malaysia.,National Water and Energy Center, United Arab Emirates University, P.O.Box: 15551, Al Ain, United Arab Emirates
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5
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Safety Maintains Lean Sustainability and Increases Performance through Fault Control. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10196851] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Almost every industrial and service enterprise adopts some form of Environmental Health and Safety (HSE) practices. However, there is no unified measurement implementation framework to resist losses exacerbated due to the “lack of safety precautions”, which must be considered one of the most dangerous Lean wastes because it jeopardizes the investment in the Hex-Bottom-Line (HBLs). Despite the widespread nature of the Lean approach, there no unified and collected framework to track and measure the effectiveness of the safety measures’ progress. Therefore, the enterprises resort to establishing their own tailored safety framework that maintains their competitiveness and sustainability. The enterprises must provide insight into safety deficiencies (i.e., faults and losses suffered) that have been measured via downtime spans and costs (Lean waste), reflecting the poor Lean Safety Performance Level (LSPL). This paper aims to shed light on two issues: (1) the adverse impact of the “lack of safety precautions” on LSPL caused by the absence of (2) a Lean Safety framework included in the Measurement and Analysis phases of Define Measure Analyze Identify Control (DMAIC). This framework is based on forecasting losses and faults according to their consumption time. The proposed framework appreciates the losses’ severity (time consumption and costs) via Fault Mode and Effect Forecasting (FMEF) aided by Artificial Neural Networks through sequential steps known as Safety Function Deployment (SFD).
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6
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Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Grain quality changes during the storage period, and an important grain quality indictor is the free fatty acid (FFA) content. Understanding real-time change of FFA content in stored grain is significant for grain storage safety. However, the FFA content requires manual detection with time-consuming and complex procedures. Thus, this paper is dedicated to developing a method to estimate FFA content in stored grain accurately. We proposed a machine learning approach—multiple-kernel support vector regression—to complete this goal, which improved the accuracy and robustness of the FFA estimation. The effectiveness of the proposed approach was validated by the grain storage data collected from northeast China. To show the merits of the proposed method, several prevailing prediction methods, such as single-kernel support vector regression, multiple linear regression, and back propagation neural network, were introduced for comparative purposes, and several quantitative statistical indexes were adopted to evaluate the performance of different models. The results showed that the proposed approach can achieve a high accuracy with mean absolute error of 0.341 mg KOH/100 g, root mean square error of 0.442 mg KOH/100 g, and mean absolute percentage error of 2.026%. Among the four models tested, the multiple-kernel support vector regression model performed best and made the most robust forecasts of FFA content in stored grain.
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7
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A Review of the Artificial Neural Network Models for Water Quality Prediction. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175776] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.
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8
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Fijani E, Barzegar R, Deo R, Tziritis E, Skordas K. Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 648:839-853. [PMID: 30138884 DOI: 10.1016/j.scitotenv.2018.08.221] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 08/15/2018] [Accepted: 08/17/2018] [Indexed: 06/08/2023]
Abstract
Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) and also least square support vector machine (LSSVM) was designed to support real-time environmental monitoring of water quality parameters, i.e. chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a Lake reservoir. Daily measurements of Chl-a and DO for June 2012-May 2013 were employed where the partial autocorrelation function was applied to screen the relevant inputs for the model construction. The variables were then split into training, validation and testing subsets where the first stage of the model testing captured the superiority of the ELM over the LSSVM algorithm. To improve these standalone predictive models, a second stage implemented a two-layer decomposition with the model inputs decomposed in the form of high and low frequency oscillations, represented by the intrinsic mode function (IMF) through the CEEMDAN algorithm. The highest frequency component, IMF1 was further decomposed with the VMD algorithm to segregate key model input features, leading to a two-layer hybrid VMD-CEEMDAN model. The VMD-CEEMDAN-ELM model was able to reduce the root mean square and the mean absolute error by about 14.04% and 7.12% for the Chl-a estimation and about 5.33% and 4.30% for the DO estimation, respectively, compared with the standalone counterparts. Overall, the developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets.
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Affiliation(s)
- Elham Fijani
- School of Geology, College of Science, University of Tehran, Tehran, Iran.
| | - Rahim Barzegar
- Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran; Department of Bioresource Engineering, McGill University, 21111 Lakeshore, Ste Anne de Bellevue, Quebec H9X3V9, Canada.
| | - Ravinesh Deo
- School of Agricultural Computational and Environmental Sciences, International Centre for Applied Climate Sciences, Institute of Agriculture and Environment, University of Southern Queensland, Springfield, Australia.
| | - Evangelos Tziritis
- Hellenic Agricultural Organization, Soil and Water Resources Institute, 57400 Sindos, Greece.
| | - Konstantinos Skordas
- Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, Fitokou street, 38446 Volos, Greece.
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9
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The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration. WATER 2018. [DOI: 10.3390/w10091124] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.
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10
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Chen Y, Yu H, Cheng Y, Cheng Q, Li D. A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture. PLoS One 2018; 13:e0192456. [PMID: 29466394 PMCID: PMC5821340 DOI: 10.1371/journal.pone.0192456] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 01/23/2018] [Indexed: 11/18/2022] Open
Abstract
A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.
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Affiliation(s)
- Yingyi Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Huihui Yu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Yanjun Cheng
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Qianqian Cheng
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Daoliang Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
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11
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Zhou C, Yang X, Zhang B, Lin K, Xu D, Guo Q, Sun C. An adaptive image enhancement method for a recirculating aquaculture system. Sci Rep 2017; 7:6243. [PMID: 28740092 PMCID: PMC5524723 DOI: 10.1038/s41598-017-06538-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 06/13/2017] [Indexed: 01/22/2023] Open
Abstract
Due to the low and uneven illumination that is typical of a recirculating aquaculture system (RAS), visible and near infrared (NIR) images collected from RASs always have low brightness and contrast. To resolve this issue, this paper proposes an image enhancement method based on the Multi-Scale Retinex (MSR) algorithm and a greyscale nonlinear transformation. First, the images are processed using the MSR algorithm to eliminate the influence of low and uneven illumination. Then, the normalized incomplete Beta function is used to perform a greyscale nonlinear transformation. The function's optimal parameters (α and β) are automatically selected by the particle swarm optimization (PSO) algorithm based on an image contrast measurement function. This adaptive image enhancement method is compared with other classic enhancement methods. The results show that the proposed method greatly improves the image contrast and highlights dark areas, which is helpful during further analysis of these images.
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Affiliation(s)
- Chao Zhou
- Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Laboratory for Agri-product Quality Traceability, Beijing, 100097, China.,School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Xinting Yang
- Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China. .,National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China. .,National Engineering Laboratory for Agri-product Quality Traceability, Beijing, 100097, China.
| | - Baihai Zhang
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Kai Lin
- Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Laboratory for Agri-product Quality Traceability, Beijing, 100097, China
| | - Daming Xu
- Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Laboratory for Agri-product Quality Traceability, Beijing, 100097, China
| | - Qiang Guo
- Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.,National Engineering Laboratory for Agri-product Quality Traceability, Beijing, 100097, China
| | - Chuanheng Sun
- Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China. .,National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China. .,National Engineering Laboratory for Agri-product Quality Traceability, Beijing, 100097, China.
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12
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Chen Y, Zhen Z, Yu H, Xu J. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture. SENSORS 2017; 17:s17010153. [PMID: 28098822 PMCID: PMC5298726 DOI: 10.3390/s17010153] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 12/18/2016] [Accepted: 01/09/2017] [Indexed: 11/25/2022]
Abstract
In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.
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Affiliation(s)
- Yingyi Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China.
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing 100083, China.
| | - Zhumi Zhen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China.
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing 100083, China.
| | - Huihui Yu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China.
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing 100083, China.
| | - Jing Xu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China.
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing 100083, China.
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