Shrimp Pond Water Temperature Forecasting System using Multiple Regression: a case study of Sam Roi Yot Subdistrict, Prachuap Khiri Khan Province
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Abstract
Shrimp farming is an important economic industry, where water temperature control plays a crucial role in the health and growth of shrimp. This research develops a tool system for collecting shrimp pond environmental data to forecast water temperature in shrimp ponds using 7 types of sensors to collect environmental data as follows: 1. Time 2. Water temperature 3. Air temperature 4. Air relative humidity 5. Water pH 6. Light intensity 7. Wind speed 8. Wind direction. The obtained data were analyzed using multiple regression to find the relationship of various factors on the forecast of water temperature in shrimp ponds. It was found that the factors with significance at the 0.05 level were:
1. Time 2. Water temperature 3. Air temperature 4. Air relative humidity 5. Light intensity. A real-time temperature forecasting system was developed, 1 hour in advance, with an accuracy of 98.2 percent, which allows farmers to promptly prepare equipment to adjust the water temperature in the pond to reduce possible losses from changes in water temperature.
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