Input Pattern According to Standard Deviation of Backpropagation Neural Network: Influence on Accuracy of Soil Moisture Retrieval

2008 ◽  
Author(s):  
Soo-See Chai ◽  
Bert Veenendaal ◽  
Geoff West ◽  
Jeffrey P. Walker
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Standard Deviation ◽  
Input Pattern ◽  
Backpropagation Neural Network

scholarly journals Coupling Normalization with Moving Window in Backpropagation Neural Network (BNN) for Passive Microwave Soil Moisture Retrieval

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Soo See Chai ◽  
Kok Luong Goh ◽  
Yee Hui Robin Chang ◽  
Kwan Yong Sim
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Standard Deviation ◽  
Time Series Data ◽  
Back Propagation ◽  
Training Data ◽  
Series Data ◽  
Moving Window ◽  
Window Method ◽  
Testing Data

AbstractA common practice to capture the non-stationary characteristics of the time series data in Artificial Neural Network (ANN) is by randomly dividing the whole set of available data into training, validation and testing, i.e. the data in validation and testing are represented in the training data. Consequently, the usability of the developed model on data not represented by the training data used during the network model development process is always doubtful. In this work, we present a back-propagation neural network (BNN) model trained using one-day history data to predict soil moisture data at 1 km resolution for two future dates. Specifically, high soil moisture values were observed in the training data while the testing data were characterized by drier conditions due to minimal or no rainfall. Our model uses separate mean and standard deviation statistics values from the training and testing data, respectively, to the z-normalized data. With data pre-processed using this method, the BNN model next uses a moving window of size 4 km × 4 km to capture the spatial variability of the soil moisture throughout the 40 km × 40 km study area. The coupling of the normalization and moving window method managed to achieve average soil moisture with Root Mean Square (RMSE) of 3.67% and correlation coefficient, R2 of 0.89. By only using the suggested normalization without the moving window method, the BNN model managed to achieve an average RMSE of barely 5.82% with R2 = 0.83. When comparing with the normal practice of using the same mean and standard deviation statistics of the training data in the testing data, the retrieval accuracy of the BNN model deteriorates to 8.86% with R2 = 0.32. The experiment results demonstrated that the proposed coupling method performed better in terms of both RMSE and R2 for soil moisture retrieval.

Design of a Soil Moisture Sensor with Temperature Compensation Using a Backpropagation Neural Network

2008 ◽  
Author(s):  
Jose Antonio Gutierrez Gnecchi ◽  
Luis Fregoso Tirado ◽  
Gerardo Marx Chavez Campos ◽  
Ruth Doñan Ramirez ◽  
Carlos Felipe Esquivel Gordillo
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Temperature Compensation ◽  
Backpropagation Neural Network ◽  
Moisture Sensor ◽  
Soil Moisture Sensor

scholarly journals Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2998
Author(s):  
Xinyong Zhang ◽  
Liwei Sun
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Bayesian Regularization ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Modal Frequency ◽  
Supporting Structure ◽  
First Order ◽  
Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

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.

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