scholarly journals Application of Convolutional Neural Network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation

2020 ◽  
Vol 12 (17) ◽  
pp. 2731
Author(s):  
Xuan-Hien Le ◽  
Giha Lee ◽  
Kwansue Jung ◽  
Hyun-uk An ◽  
Seungsoo Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Convolutional Neural Network ◽  
Bias Correction ◽  
Temporal Correlation ◽  
Precipitation Data ◽  
Climate Data ◽  
Extreme Rainfall Events ◽  
Essential Components ◽  
Deviation Method

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.

scholarly journals Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4490 ◽  
Author(s):  
Jaeik Jeong ◽  
Hongseok Kim
Keyword(s):  
Neural Network ◽  
New York ◽  
Convolutional Neural Network ◽  
Smart Grids ◽  
Power Plants ◽  
Temporal Correlation ◽  
Space Time ◽  
Percentage Error ◽  
Virtual Power Plants ◽  
Pv Generation

The accurate forecasting of photovoltaic (PV) power generation is critical for smart grids and the renewable energy market. In this paper, we propose a novel short-term PV forecasting technique called the space-time convolutional neural network (STCNN) that exploits the location information of multiple PV sites and historical PV generation data. The proposed structure is simple but effective for multi-site PV forecasting. In doing this, we propose a greedy adjoining algorithm to preprocess PV data into a space-time matrix that captures spatio-temporal correlation, which is learned by a convolutional neural network. Extensive experiments with multi-site PV generation from three typical states in the US (California, New York, and Alabama) show that the proposed STCNN outperforms the conventional methods by up to 33% and achieves fairly accurate PV forecasting, e.g., 4.6–5.3% of the mean absolute percentage error for a 6 h forecasting horizon. We also investigate the effect of PV sites aggregation for virtual power plants where errors from some sites can be compensated by other sites. The proposed STCNN shows substantial error reduction by up to 40% when multiple PV sites are aggregated.

scholarly journals Klasifikasi Makna Tangisan Bayi Menggunakan CNN Berdasarkan Kombinasi Fitur MFCC dan DWT

2021 ◽  
Vol 8 (2) ◽  
pp. 599-610
Author(s):  
Yohannes Yohannes ◽  
Ricky Wijaya
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Convolutional Neural Network ◽  
Standard Deviation Range

Menangis merupakan cara bayi untuk berkomunikasi. Suara tangisan tersebut dapat digunakan untuk mengidentifikasi masalah pada bayi, seperti kelaparan, kesakitan, rasa kantuk, kelelahan, rasa tidak nyaman, kedinginan atau kepanasan, dan lain-lain. Namun, tidak semua orang dapat mengenali makna tangisan bayi tersebut. Kombinasi dari fitur MFCC dan DWT digunakan pada penelitian ini sebagai fitur ekstraksi pada suara tangisan bayi. Pada penelitian ini, metode Convolutional Neural Network (CNN) digunakan untuk klasifikasi makna suara dari tangisan bayi. Dataset yang digunakan pada penelitian ini adalah dataset publik yang terdiri dari total 61 data training dan 30 data testing. Jenis tangisan bayi yang digunakan pada penelitian ini adalah lapar, lelah, rasa tidak nyaman, dan sakit perut. Berdasarkan hasil pengujian, fitur MFCC dan CNN didapatkan precision sebesar 32,76%, recall sebesar 32,63%, dan accuracy sebesar 73,33%. Kombinasi fitur MFCC dan DWT (Mean, Standard Deviation, Range, Max) dan CNN didapatkan precision sebesar 50,91%, recall sebesar 44,23%, dan accuracy sebesar 73,33%.

scholarly journals ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting

2021 ◽  
Author(s):  
Yu Huang ◽  
James Li ◽  
Min Shi ◽  
Hanqi Zhuang ◽  
Yufei Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Temporal Dynamics ◽  
Temporal Correlation ◽  
Current Data ◽  
Ocean Current ◽  
Coupled Neural Networks ◽  
Proposed Model ◽  
Essential Components ◽  
Spatio Temporal ◽  
Learn Physics

Abstract Ocean current, fluid mechanics, and many other physical systems with spatio-temporal dynamics are essential components of the universe. One key characteristic of such systems is that they can be represented by certain physics laws, such as ordinary/partial differential equations (ODEs/PDEs), irrespective of time or location. Physics-informed machine learning has recently emerged to learn physics from data for accurate prediction, but they often lack a mechanism to leverage localized spatial and temporal correlation or rely on hard-coded physics parameters. In this paper, we advocate a physics-coupled neural network model to learn parameters governing the physics of the system, and further couple the learned physics to assist the learning of recurring dynamics. Here a spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system. The physics-coupled learning ensures that the proposed model can be tremendously improved by using learned physics parameters, and can achieve useful long-range forecasting (e.g., more than two weeks). Experiments using simulated wave propagation and field-collected ocean current data validate that ST-PCNN outperforms typical deep learning models and existing physics-informed models.

scholarly journals Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1609 ◽  
Author(s):  
Sungjae Lee ◽  
Yung-Seop Lee ◽  
Youngdoo Son
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Weather Forecasting ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Maximum Temperature ◽  
Time Interval ◽  
Climate Data ◽  
Temperature Forecasting

Temperature forecasting has been a consistent research topic owing to its significant effect on daily lives and various industries. However, it is an ever-challenging task because temperature is affected by various climate factors. Research on temperature forecasting has taken one of two directions: time-series analysis and machine learning algorithms. Recently, a large amount of high-frequent climate data have been well-stored and become available. In this study, we apply three types of neural networks, multilayer perceptron, recurrent, and convolutional, to daily average, minimum, and maximum temperature forecasting with higher-frequency input features than researchers used in previous studies. Applying these neural networks to the observed data from three locations with different climate characteristics, we show that prediction performance with highly frequent hourly input data is better than forecasting performance with less-frequent daily inputs. We observe that a convolutional neural network, which has been mostly employed for processing satellite images rather than numeric weather data for temperature forecasting, outperforms the other models. In addition, we combine state of the art weather forecasting techniques with the convolutional neural network and evaluate their effects on the temperature forecasting performances.

scholarly journals MULTI-TEMPORAL SAR IMAGE DESPECKLING BASED A CONVOLUTIONAL NEURAL NETWORK

2020 ◽  
Vol V-5-2020 ◽  
pp. 101-107
Author(s):  
C. Zhou ◽  
J. Li ◽  
H. Shen ◽  
Q. Yuan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spatial Information ◽  
Intrinsic Property ◽  
Temporal Correlation ◽  
Real Data ◽  
Sufficient Information ◽  
Sar Images ◽  
Multi Temporal ◽  
Sar Imagery

Abstract. Speckle noise is an intrinsic property of Synthetic Aperture Radar (SAR) imagery, which affects the quality of image. Single-temporal despeckling methods usually pay attention to the utilization of spatial information, but sometimes due to lack of sufficient information, the despeckling image is too smooth or losses some information about edge details. However, multi-temporal SAR images can provide extra information for despeckling resulting in better performance. Therefore, in this paper, we proposed a novel multi-temporal SAR despeckling method based a convolutional neural network (MSAR-CNN) embedded temporal and spatial attention (TSA) module to deeply mine the spatial and temporal correlation of multitemporal SAR images. The whole network, which is end-to-end trained with simulate realistic SAR data, consists of several residual blocks. In addition, the simulated and real-data experiments demonstrate that the proposed MSAR-CNN outperforms most of the mainstream methods in both the quantitative evaluation indexes and visual effects.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
Sign in / Sign up

Export Citation Format

Share Document