臺灣博碩士論文加值系統

此研究提出一個方法，讓人們能在無氣象觀測站的環境下，只要手持能受到無線電波信號的移動設備，就可以藉由所收到的無線電波信號強度(RSSI)因雨量所受到的衰減，來推估移動設備位置可能的降雨量。

在過去的研究中，常利用雨量對無線電波信號的衰減來做補償，此外，也有少部分文獻是藉由訊號強度來估測雨量活其他天氣因素，但存在一些限制像是需要該基地台和移動設備間的距離，和需要該基地台發出的原始信號強度才能進行估測。在本研究中，則不需要得知距離和原始信號強度，只單純需要利用信號的強度來進行估測。

本研究能夠補足氣象觀測站設置密度的不足，降低無氣象觀測站地區雨量估測的估測誤差，使人們能知道所在地的降雨量，並作相對應的防護措施，降低降雨可能導致的財產損失或保障自己的生命安全。

This study proposes a method, that people just hold mobile device which can received signal strength indicator (RSSI) of GSM, Then, based on RSSI reduction suffered by rain, the method can estimate rain rate in mobile device location, even the environment of location is no weather station.

In the past research, some of them use rain rate to compensate rain attenuation of RSSI. The other research use rain attenuation of RSSI with the distance between base station and mobile device to estimate rain rate. In this study, we only use the RSSI measured by mobile device to estimate rain rate different from other research.

This study is able to make up inadequate of density form weather stations. Reduce estimation error of rain rate in no weather stations environment. So people can know rain rate of their area and prevent disaster by rain rate. People can reduce the damage and protect their lives from the disaster of rain rate.

Contents.....................vii
List of Table ................ix
List of Figure................xi
Cahpter1. 緒論..................2
1.1 研究背景及動機目的.......2
1.2 相關文獻................4
1.3 論文架構................5
Cahpter2. 理論基礎..............7
2.1 線性回歸................7
2.2 穩健回歸................7
2.3 支持向量機..............8
2.4 類神經網路..............9
Cahpter3. 提出的方法.............11
3.1 無氣象觀測站環境..........11
3.2 無需得知基地台位置........11
3.3 方法流程圖...............12
Cahpter4. 實驗設置與資料處理......15
4.1 實驗設置.................15
4.2 實驗器材和實驗環境設置....16
4.3 實驗資料處理.............22
Cahpter5. 雨量估測結果與分析......30
5.1 有氣象觀測站環境的雨量估測.30
5.2 無氣象觀測站環境的雨量估測.38
Cahpter6. 結論與未來展望..........47
Bibliography.....................48

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