Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept
Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2023-11-01
|
| Series: | Journal of Hydroinformatics |
| Subjects: | |
| Online Access: | http://jhydro.iwaponline.com/content/25/6/2660 |
| _version_ | 1797428874344136704 |
|---|---|
| author | Cheng-Chia Huang Che-Cheng Chang Chiao-Ming Chang Ming-Han Tsai |
| author_facet | Cheng-Chia Huang Che-Cheng Chang Chiao-Ming Chang Ming-Han Tsai |
| author_sort | Cheng-Chia Huang |
| collection | DOAJ |
| description | Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site. This study firstly uses mean-absolute-error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and Nash–Sutcliffe efficiency coefficient (NSE) to describe the performance of the proposed convolutional neural network (CNN). Moreover, adapting the ensemble learning concept to compare the multiple machine learning (ML) approaches, the CNN presents the highest predicted accuracy, 91%, better than SVM (79%), VGG19 (63%) and ResNet50 (35%). As a result, the proposed CNN framework can appropriately apply the monitoring needs. The primary purpose is to develop a simple, accurate, and stable SC monitoring technology. Instead of some complex architectures, a simple and small neural network is adopted to implement real-time application (RTA). Via our design, such a traditional but critical issue can be improved to a new state. For example, by incorporating the concept of the Internet of Things (IoT) with our design, the distributed computing system for large-scale environmental monitoring can be realized quickly and easily.
HIGHLIGHTS
Safety: Contactless technology.;
Simple: Improving the computation efficiency for real-time prediction.;
Accuracy: Presenting satisfied accuracy by comparing it with the current technology.;
Stability: Overcoming outlier trouble in the prediction process to show sufficient stability.;
Development: Incorporating the IoT to develop the large-scale environmental monitoring platform quickly and easily.; |
| first_indexed | 2024-03-09T09:05:10Z |
| format | Article |
| id | doaj.art-79babc7e4be547e9a6552a1c01d4263e |
| institution | Directory Open Access Journal |
| issn | 1464-7141 1465-1734 |
| language | English |
| last_indexed | 2024-03-09T09:05:10Z |
| publishDate | 2023-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Hydroinformatics |
| spelling | doaj.art-79babc7e4be547e9a6552a1c01d4263e2023-12-02T10:28:13ZengIWA PublishingJournal of Hydroinformatics1464-71411465-17342023-11-012562660267410.2166/hydro.2023.215215Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT conceptCheng-Chia Huang0Che-Cheng Chang1Chiao-Ming Chang2Ming-Han Tsai3 Department of Water Resources Engineering and Conservation, Feng Chia University, Taichung City, Chinese Taipei Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Chinese Taipei Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Chinese Taipei Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Chinese Taipei Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site. This study firstly uses mean-absolute-error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and Nash–Sutcliffe efficiency coefficient (NSE) to describe the performance of the proposed convolutional neural network (CNN). Moreover, adapting the ensemble learning concept to compare the multiple machine learning (ML) approaches, the CNN presents the highest predicted accuracy, 91%, better than SVM (79%), VGG19 (63%) and ResNet50 (35%). As a result, the proposed CNN framework can appropriately apply the monitoring needs. The primary purpose is to develop a simple, accurate, and stable SC monitoring technology. Instead of some complex architectures, a simple and small neural network is adopted to implement real-time application (RTA). Via our design, such a traditional but critical issue can be improved to a new state. For example, by incorporating the concept of the Internet of Things (IoT) with our design, the distributed computing system for large-scale environmental monitoring can be realized quickly and easily. HIGHLIGHTS Safety: Contactless technology.; Simple: Improving the computation efficiency for real-time prediction.; Accuracy: Presenting satisfied accuracy by comparing it with the current technology.; Stability: Overcoming outlier trouble in the prediction process to show sufficient stability.; Development: Incorporating the IoT to develop the large-scale environmental monitoring platform quickly and easily.;http://jhydro.iwaponline.com/content/25/6/2660convolutional neural network (cnn)distributed computing systeminternet of things (iot)real-time application (rta)sediment concentration (sc) |
| spellingShingle | Cheng-Chia Huang Che-Cheng Chang Chiao-Ming Chang Ming-Han Tsai Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept Journal of Hydroinformatics convolutional neural network (cnn) distributed computing system internet of things (iot) real-time application (rta) sediment concentration (sc) |
| title | Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept |
| title_full | Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept |
| title_fullStr | Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept |
| title_full_unstemmed | Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept |
| title_short | Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept |
| title_sort | development of a lightweight convolutional neural network based visual model for sediment concentration prediction by incorporating the iot concept |
| topic | convolutional neural network (cnn) distributed computing system internet of things (iot) real-time application (rta) sediment concentration (sc) |
| url | http://jhydro.iwaponline.com/content/25/6/2660 |
| work_keys_str_mv | AT chengchiahuang developmentofalightweightconvolutionalneuralnetworkbasedvisualmodelforsedimentconcentrationpredictionbyincorporatingtheiotconcept AT chechengchang developmentofalightweightconvolutionalneuralnetworkbasedvisualmodelforsedimentconcentrationpredictionbyincorporatingtheiotconcept AT chiaomingchang developmentofalightweightconvolutionalneuralnetworkbasedvisualmodelforsedimentconcentrationpredictionbyincorporatingtheiotconcept AT minghantsai developmentofalightweightconvolutionalneuralnetworkbasedvisualmodelforsedimentconcentrationpredictionbyincorporatingtheiotconcept |