Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds
The cold weather-related economic losses in the aquaculture and fisheries industries are enormous and will only increase due to future climate change. Advancements in weather forecasting have increased the accuracy of predicting environmental factors like air temperature, solar radiation, and wind s...
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524001667 |
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| author | Hsiang-Hsuan Li Tsun-Hua Yang Chin-Cheng Tsai |
| author_facet | Hsiang-Hsuan Li Tsun-Hua Yang Chin-Cheng Tsai |
| author_sort | Hsiang-Hsuan Li |
| collection | DOAJ |
| description | The cold weather-related economic losses in the aquaculture and fisheries industries are enormous and will only increase due to future climate change. Advancements in weather forecasting have increased the accuracy of predicting environmental factors like air temperature, solar radiation, and wind speed. However, the water temperature of fishponds, which affects the lives of fish, cannot be accurately predicted. As a result, fishermen are unable to implement early disaster mitigation and avoidance measures effectively. In this study, we developed an early warning system for extreme temperature events in fishponds by using a weather forecasting model in combination with local observations from a customized sensor placed in a pond. This system could provide water temperature forecasts with up to 120 h of lead time. A fishpond and multiple events were selected to assess the performance. Compared to the actual observations, the predicted water temperature difference had a root mean square error of <2 °C for up to 72 h of lead time. Furthermore, due to limited computational resources for weather forecasting models, the water temperature and depth data collected by the sensor improved the accuracy of temperature prediction specific to each pond. The results have confirmed that the integrated method can effectively predict the water temperature of farmed fishponds and assist fishermen in implementing precautionary measures in time. |
| format | Article |
| id | doaj-art-10ddc1a0b0c545239cfd9d35ac2193fe |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-10ddc1a0b0c545239cfd9d35ac2193fe2025-08-20T02:38:23ZengElsevierSmart Agricultural Technology2772-37552024-12-01910056110.1016/j.atech.2024.100561Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishpondsHsiang-Hsuan Li0Tsun-Hua Yang1Chin-Cheng Tsai2National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu City, 300093, Taiwan, Republic of ChinaNational Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu City, 300093, Taiwan, Republic of China; Corresponding author.Central Weather Administration, 64, Gongyuan Road, Taipei 100006, Taiwan, Republic of China; National Taiwan University, 1, Sec. 4, Roosevelt Road, Taipei 106319, Taiwan, Republic of ChinaThe cold weather-related economic losses in the aquaculture and fisheries industries are enormous and will only increase due to future climate change. Advancements in weather forecasting have increased the accuracy of predicting environmental factors like air temperature, solar radiation, and wind speed. However, the water temperature of fishponds, which affects the lives of fish, cannot be accurately predicted. As a result, fishermen are unable to implement early disaster mitigation and avoidance measures effectively. In this study, we developed an early warning system for extreme temperature events in fishponds by using a weather forecasting model in combination with local observations from a customized sensor placed in a pond. This system could provide water temperature forecasts with up to 120 h of lead time. A fishpond and multiple events were selected to assess the performance. Compared to the actual observations, the predicted water temperature difference had a root mean square error of <2 °C for up to 72 h of lead time. Furthermore, due to limited computational resources for weather forecasting models, the water temperature and depth data collected by the sensor improved the accuracy of temperature prediction specific to each pond. The results have confirmed that the integrated method can effectively predict the water temperature of farmed fishponds and assist fishermen in implementing precautionary measures in time.http://www.sciencedirect.com/science/article/pii/S2772375524001667WRFIoTEarly warning systemExtreme temperature |
| spellingShingle | Hsiang-Hsuan Li Tsun-Hua Yang Chin-Cheng Tsai Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds Smart Agricultural Technology WRF IoT Early warning system Extreme temperature |
| title | Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds |
| title_full | Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds |
| title_fullStr | Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds |
| title_full_unstemmed | Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds |
| title_short | Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds |
| title_sort | integration of the wrf model and iot sensors to develop an early cold snap warning system for inland fishponds |
| topic | WRF IoT Early warning system Extreme temperature |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524001667 |
| work_keys_str_mv | AT hsianghsuanli integrationofthewrfmodelandiotsensorstodevelopanearlycoldsnapwarningsystemforinlandfishponds AT tsunhuayang integrationofthewrfmodelandiotsensorstodevelopanearlycoldsnapwarningsystemforinlandfishponds AT chinchengtsai integrationofthewrfmodelandiotsensorstodevelopanearlycoldsnapwarningsystemforinlandfishponds |