stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
郭隨安 , Masters Advisor：林國峰
繁體中文 DOI： 10.6342/NTU201802045
- 1. Ahijevych, D., Pinto, J.O., Williams, J.K., Steiner, M., 2016. Probabilistic Forecasts of Mesoscale Convective System Initiation Using the Random Forest Data Mining Technique. Weather and Forecasting 31, 581–599.
- 2. Amengual, A., Romero, R., Alonso, S., 2008. Hydrometeorological ensemble simulations of flood events over a small basin of Majorca Island, Spain. Quarterly Journal of the Royal Meteorological Society 134, 1221–1242.
- 3. Anctil, F., Michel, C., Perrin, C., Andréassian, V., 2004. A soil moisture index as an auxiliary ANN input for stream flow forecasting. Journal of Hydrology 286, 155–167.
- 4. Azmat, M., Laio, F., Poggi, D., 2015. Estimation of Water Resources Availability and Mini-Hydro Productivity in High-Altitude Scarcely-Gauged Watershed. Water Resources Management 29, 5037–5054.
- 5. Ba, H., Guo, S., Wang, Y., Hong, X., Zhong, Y., Liu, Z., 2017. Improving ANN model performance in runoff forecasting by adding soil moisture input and using data preprocessing techniques. Hydrology Research 49, 744–760.
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