Nowcasting and Short Term GHI Forecasting Using GOES-16 Shortwave Radiance Data: A Machine Learning Case Study of Petrolina-PE, Brazil

The paper presents a hybrid approach for short-term river flood forecasting. It is based on multi-modal data fusion from different sources (weather stations, water height sensors, remote sensing data). To improve the forecasting efficiency, the machine learning methods and the Snowmelt-Runoff physical model are combined in a composite modeling pipeline using automated machine learning techniques. The novelty of the study is based on the application of automated machine learning to identify the individual blocks of a composite pipeline without involving an expert. It makes it possible to adapt the approach to various river basins and different types of floods. Lena River basin was used as a case study since its modeling during spring high water is complicated by the high probability of ice-jam flooding events. Experimental comparison with the existing methods confirms that the proposed approach reduces the error at each analyzed level gauging station. The value of Nash–Sutcliffe model efficiency coefficient for the ten stations chosen for comparison is 0.80. The other approaches based on statistical and physical models could not surpass the threshold of 0.74. Validation for a high-water period also confirms that a composite pipeline designed using automated machine learning is much more efficient than stand-alone models.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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Emplacing Berlin: Digital Itineraries and Short-term Study Abroad in the City

International Journal of Humanities and Arts Computing ◽

10.3366/ijhac.2018.0204 ◽

2018 ◽

Vol 12 (1) ◽

pp. 26-36 ◽

Cited By ~ 1

Author(s):

Richard B. Apgar

Keyword(s):

Study Abroad ◽

Urban Landscape ◽

Physical Space ◽

Short Term ◽

Study Abroad Programs ◽

Term Study ◽

Digital Maps ◽

Short Term Study ◽

The City

As destination of choice for many short-term study abroad programs, Berlin offers students of German language, culture and history a number of sites richly layered with significance. The complexities of these sites and the competing narratives that surround them are difficult for students to grasp in a condensed period of time. Using approaches from the spatial humanities, this article offers a case study for enhancing student learning through the creation of digital maps and itineraries in a campus-based course for subsequent use during a three-week program in Berlin. In particular, the concept of deep mapping is discussed as a means of augmenting understanding of the city and its history from a narrative across time to a narrative across the physical space of the city. As itineraries, these course-based projects were replicated on site. In moving from the digital environment to the urban landscape, this article concludes by noting meanings uncovered and narratives formed as we moved through the physical space of the city.

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