scholarly journals Crop Classification by Machine Learning Algorithm Combined X-band and C-band SAR Data

2020 ◽  
Vol 59 (6) ◽  
pp. 259-274
Author(s):  
Yuki YAMAYA ◽  
Rei SONOBE ◽  
Nobuyuki KOBAYASHI ◽  
Kan-ichiro MOCHIZUKI ◽  
Xiufeng WANG ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
X Band ◽  
Crop Classification ◽  
Sar Data

scholarly journals Crop Classification by Machine Learning Algorithm Using C-band SAR Data

2017 ◽  
Vol 56 (4) ◽  
pp. 143-148
Author(s):  
Yuki YAMAYA ◽  
Hiroshi TANI ◽  
Xiufeng WANG ◽  
Rei SONOBE ◽  
Nobuyuki KOBAYASHI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Crop Classification ◽  
Sar Data

scholarly journals Crop Classification by Machine Learning Algorithm with Combination of X- and C-band SAR Data

2018 ◽  
Vol 57 (2) ◽  
pp. 78-83
Author(s):  
Yuki YAMAYA ◽  
Hiroshi TANI ◽  
Xiufeng WANG ◽  
Rei SONOBE ◽  
Nobuyuki KOBAYASHI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Crop Classification ◽  
Sar Data

scholarly journals High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 SAR data and machine learning

2021 ◽  
Author(s):  
Louis de Montera ◽  
Henrick Berger ◽  
Romain Husson ◽  
Pascal Appelghem ◽  
Laurent Guerlou ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Wind Speed ◽  
Numerical Model ◽  
Wind Power ◽  
Learning Algorithm ◽  
Offshore Wind ◽  
Machine Learning Algorithm ◽  
Power Maps ◽  
Sar Data

Abstract. This paper presents a method to calculate offshore wind power at turbine hub height from Sentinel-1 Synthetic Aperture Radar (SAR) data using machine learning. The method is tested in two 70 km × 70 km areas off the Dutch coast where Lidar measurements are available. Firstly, SAR winds at surface level are improved with a machine learning algorithm using geometrical characteristics of the sensor and parameters related to the atmospheric stability extracted from a high-resolution numerical model. The wind speed bias at 10 m above sea level is reduced from −0.42 m s−1 to 0.02 m s−1 and its standard deviation from 1.41 m s−1 to 0.98 m s−1. After improvement, SAR surface winds are extrapolated at higher altitudes with a separate machine learning algorithm trained with the wind profiles measured by the Lidars. We show that, if profiling Lidars are available in the area of study, these two steps can be combined into a single one, in which the machine learning algorithm is trained directly at turbine hub height. Once the wind speed at turbine hub height is obtained, the extractible wind power is calculated using the method of the moments and a Weibull distribution. The results are given assuming an 8 MW turbine typical power curve. The accuracy of the wind power derived from SAR data is in the range ±3–4 % when compared with Lidars. Then, wind power maps at 200 m are presented and compared with the raw outputs of the numerical model at the same altitude. The maps based on SAR data have a much better level of detail, in particular regarding the coastal gradient. The new revealed patterns show differences with the numerical of as much as 10 % in some locations. We conclude that SAR data combined with a high-resolution numerical model and machine learning techniques can improve the wind power estimation at turbine hub height, and thus provide useful insights for optimizing wind farm siting and risk management.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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