Daily rainfall forecast model from satellite image using Convolution neural network

2018 ◽  
Author(s):  
Kitinan Boonyuen ◽  
Phisan Kaewprapha ◽  
Patchanok Srivihok
Keyword(s):  
Neural Network ◽  
Satellite Image ◽  
Daily Rainfall ◽  
Forecast Model ◽  
Convolution Neural Network ◽  
Rainfall Forecast

Prediction of Rainfall in Da-Dong-Yong Hydrologic Station Based on Wavelet Neural Network

2013 ◽  
Vol 726-731 ◽  
pp. 3279-3282 ◽  
Author(s):  
Xiu Li Sang ◽  
Yu Zhen Su ◽  
Han Jie Xiao ◽  
Hua Wang ◽  
Jian Xin Xu
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Daily Rainfall ◽  
Wavelet Neural Network ◽  
Rainfall Data ◽  
Rainfall Prediction ◽  
Daily Rainfall Data ◽  
Rainfall Forecast ◽  
Forecast Data ◽  
Accuracy And Stability

This paper aims to seek a way for improving rainfall prediction accuracy from the perspective of time unit points which are 5 days, 10 days, 15 days, 20 days, 25 days, and 30 days. Based on the daily rainfall data from 2001 to 2010 of Da-dong-yong hydrologic station, the rainfalls are predicted by establishing the model of wavelet neural network. Results show that prediction accuracy and stability of time unit points is 30-day > 25-day > 15-day > 10-day > 20-day > 5-day. The trend of six kinds of rainfall forecast is consistent. When the number of forecast data is fewer and time unit point is longer, the accuracy and stability of rainfall forecast are better.

scholarly journals Bare Skin Image Classification using Convolution Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 138-145
Author(s):  
Jaya Gupta ◽  
◽  
Sunil Pathak ◽  
Gireesh Kumar
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Satellite Image ◽  
Activation Function ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Significant Research ◽  
Research Problems ◽  
Facial Expression Classification ◽  
Expression Classification

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, and plant classification based on images. Here in the paper, the image classification model is applied for identifying the display of daunting pictures on the internet. The proposed model uses Convolution neural network to identify these images and filter them through different blocks of the network, so that it can be classified accurately. The model will work as an extension to the web browser and will work on all websites when activated. The extension will be blurring the images and deactivating the links on web pages. This means that it will scan the entire web page and find all the daunting images present on that page. Then we will blur those images before they are loaded and the children could see them. Keywords— Activation Function, CNN, Images Classification , Optimizers, VGG-19

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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