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標題: | 應用Hydrus-1D、ICON以及隨機森林預測溫室內土壤溫度及體積水分含量之變化 Soil Temperature and Volumetric Water Content Prediction in Greenhouse by Using Hydrus-1D, ICON and Random Forest Method |
作者: | Kan-Sheng Hsu 許堪昇 |
指導教授: | 許少瑜 |
關鍵字: | Hydrus-1D,ICON,隨機森林,土壤溫度,體積水分含量, Hydrus-1D,ICON,random forest,soil temperature,volumetric water content, |
出版年 : | 2019 |
學位: | 碩士 |
摘要: | 在氣候變遷之影響下,極端天氣之頻率上升危及人類糧食及水資源之問題日益嚴重,衍伸出許多問題。由於溫室環境較不受外在因子影響且容易管理之優勢,溫室內之精準農業逐漸成為趨勢。為了有效提高水資源之利用且減少水資源之浪費,本研究在溫室內種植玉女小番茄並裝設監控儀器收集數據,希望藉由結合天氣預報對作物生長期間之土壤溫度及體積水分含量做預測。
首先,分別採用物理模式(Hydrus-1D)、機器學習(隨機森林)及動態拓樸學(ICON)的方法,模擬溫室內土壤溫度及體積含水量隨時間變化之情形。結果發現,這三種模式在模擬階段皆表現相當,均可以有效模擬溫室內之土壤溫度及體積水分含量。更進一步利用天氣預報資料,結合三種模式,預測土壤溫度及體積水分含量。 在物理模式下,所預測之土壤溫度趨勢均相似;且預測體積水分含量也較為準確,但卻無法預測人為澆灌之水分含量上升。在動態拓樸學模式下,預測土壤溫度的效果不彰。以機器學習之模式進行預測,不論是在土壤溫度或者體積水分含量之變化,皆可以得到最好之結果。 本研究應用Hydrus-1D、ICON以及隨機森林模擬並預測溫室內土壤溫度及體積水分含量之變化,能提供溫室管理之決策依據,達到智慧農業或無人農業之目標,同時提高水資源使用效率。 Due to the impact of climate change and the rising frequency of extreme weather have caused the problem of food security and water resources. Precision agriculture in greenhouses is gradually becoming a trend because it is less affected by external environmental factors and easier to manage. In order to effectively improve the utilization of water resources and reduce wasting water, this study planted cherry tomatoes in the greenhouse and installed monitoring instruments to collect data. The soil temperature and volumetric water content during tomato growth can be predicted effectively by combining the weather forecast. The Hydrus-1D, random forest method and ICON were used to simulate the change of soil temperature and volumetric water content in the greenhouse. The results of the simulation show good performances among the three models. Further combined with weather forecast data, three models were applied to predict soil temperatures and volumetric moisture contents. Both of predicted soil temperature and volumetric water content by Hydrus-1D followed the trend of real measured data, but Hydrus-1D can’t predict the watering situation. The predicted performance of soil temperature by ICON was low. Nevertheless, both predictions by random forest method were better than others. This study simulated and predicted the soil temperatures and volumetric water contents by applying Hydrus-1D, random forest method and ICON to improve greenhouse management and increase water-use efficiency. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73714 |
DOI: | 10.6342/NTU201903914 |
全文授權: | 有償授權 |
顯示於系所單位: | 生物環境系統工程學系 |
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