scholarly journals Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

2018 ◽  
Vol 10 (7) ◽  
pp. 2474 ◽  
Author(s):  
Li Zhao ◽  
Yue-Ming Hu ◽  
Wu Zhou ◽  
Zhen-Hua Liu ◽  
Yu-Chun Pan ◽  
...  
Keyword(s):  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Scientific Basis ◽  
Mercury Content ◽  
Hyperspectral Data ◽  
Estimation Methods ◽  
Algorithm Optimization ◽  
Toxic Heavy Metals ◽  
Soil Mercury

Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

scholarly journals Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models

2020 ◽  
Vol 12 (12) ◽  
pp. 1998
Author(s):  
Lijuan Cui ◽  
Zhiguo Dou ◽  
Zhijun Liu ◽  
Xueyan Zuo ◽  
Yinru Lei ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Model Error ◽  
Scientific Basis ◽  
Hyperspectral Data ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Nitrogen And Phosphorus ◽  
Accuracy And Stability

Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R2 of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future.

scholarly journals Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

2019 ◽  
Vol 11 (2) ◽  
pp. 419 ◽  
Author(s):  
Piao Liu ◽  
Zhenhua Liu ◽  
Yueming Hu ◽  
Zhou Shi ◽  
Yuchun Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Hyperspectral Data ◽  
Estimation Accuracy ◽  
Soil Heavy Metals ◽  
Soil Heavy Metal ◽  
Metal Contents

Soil heavy metals affect human life and the environment, and thus, it is very necessary to monitor their contents. Substantial research has been conducted to estimate and map soil heavy metals in large areas using hyperspectral data and machine learning methods (such as neural network), however, lower estimation accuracy is often obtained. In order to improve the estimation accuracy, in this study, a back propagation neural network (BPNN) was combined with the particle swarm optimization (PSO), which led to an integrated PSO-BPNN method used to estimate the contents of soil heavy metals: Cd, Hg, and As. This study was conducted in Guangdong, China, based on the soil heavy metal contents and hyperspectral data collected from 90 soil samples. The prediction accuracies from BPNN and PSO-BPNN were compared using field observations. The results showed that, 1) the sample averages of Cd, Hg, and As were 0.174 mg/kg, 0.132 mg/kg, and 9.761 mg/kg, respectively, with the corresponding maximum values of 0.570 mg/kg, 0.310 mg/kg, and 68.600 mg/kg being higher than the environment baseline values; 2) the transformed and combined spectral variables had higher correlations with the contents of the soil heavy metals than the original spectral data; 3) PSO-BPNN significantly improved the estimation accuracy of the soil heavy metal contents, with the decrease in the mean relative error (MRE) and relative root mean square error (RRMSE) by 68% to 71%, and 64% to 67%, respectively. This indicated that the PSO-BPNN provided great potential to estimate the soil heavy metal contents; and 4) with the PSO-BPNN, the Cd content could also be mapped using HuanJing-1A Hyperspectral Imager (HSI) data with a RRMSE value of 36%, implying that the PSO-BPNN method could be utilized to map the heavy metal content in soil, using both field spectral data and hyperspectral imagery for the large area.

Natural Gas Load Forecasting Based on Improved Back Propagation Neural Network

2014 ◽  
Vol 563 ◽  
pp. 312-315
Author(s):  
Yu Lian Jiang
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Relative Error ◽  
Load Forecasting ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Back Propagation Algorithm ◽  
Traditional Algorithm ◽  
Improved Algorithm

To suit for the condition that the relative error is more popular than the absolute error, and overcome the shortcoming of the traditional Back propagation neural network, this paper proposed an improved Back propagation algorithm with additional momentum item based on the sum of relative error square. The improved algorithm was applied to the example of the natural gas load forecasting, simulations showed that the improved algorithm has faster training speed than the traditional algorithm, and has higher accuracy as while.

scholarly journals Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data

2019 ◽  
Vol 9 (21) ◽  
pp. 4620 ◽  
Author(s):  
Deng ◽  
Zhang ◽  
Cen
Keyword(s):  
Chemical Oxygen Demand ◽  
Back Propagation ◽  
Oxygen Demand ◽  
Back Propagation Neural Network ◽  
Hyperspectral Data ◽  
Bp Algorithm ◽  
A Value ◽  
Regression Relationship ◽  
Network Method ◽  
Regression Problems

This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and COD values. The results indicate there is a statistically significant correlation between COD and the hyperspectral data. The accuracy of the capsule network was compared with the results obtained from using a traditional back-propagation neural network (BP) method. The capsule network achieved superior accuracy with fewer iterations, compared with the BP algorithm. An R2 value of 0.78 was obtained against measured COD values retrieved using the capsule network method, compared with a value of 0.42 for the BP algorithm retrievals. This suggests the capsule network method has great potential to solve regression problems in the field of remote sensing.

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’s Transformation

2016 ◽  
Vol 818 ◽  
pp. 156-165 ◽  
Author(s):  
Makmur Saini ◽  
Abdullah Asuhaimi bin Mohd Zin ◽  
Mohd Wazir Bin Mustafa ◽  
Ahmad Rizal Sultan ◽  
Rahimuddin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Comparison Of The Results

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

scholarly journals Regional Short-term Micro-climate Air Temperature Prediction with CBPNN

2018 ◽  
Vol 53 ◽  
pp. 03009 ◽  
Author(s):  
Lvwen Huang ◽  
Lianliang Chen ◽  
Qin Wang ◽  
Siwen Yan ◽  
Xunbing Gao ◽  
...  
Keyword(s):  
Air Temperature ◽  
Stepwise Regression ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Climate Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Air Temperature Prediction

This paper proposes a novel short-term air temperature prediction with three-layer Back Propagation Neural Network (BPNN) for the regional application of next 1-12 hours. With the continuous collection of eight real-time micro-climate parameters in the experimentation and demonstration stations in our university, the Multiple Stepwise Regression (MSR) is employed to screen the original historical data to find the parameter factors with greater contribution rate. On the basis of the Root Mean Square Error (RMSE) value evaluating the optimal fitting degree of the stepwise regression, the Levenberg-Marquardt (LM) and the Resilient Propagation (R-Prop) training algorithm are employed to construct a Combined BPNN (CBPNN) with two MSR inputs. Compared with the known micro-climate data sets, the Mean Absolute Error (MAE) is to evaluate the applicability of CBPNN prediction model. The experimentation shows that the MAE is within 4°C in the next 12 hours. This proposal will be deployed in stations in our university for extreme weather warnings, and could be applied to some regional short-term parameter prediction for the future agricultural production service.

scholarly journals EXTREME RAINFALL FORECASTING MODEL BASED ON DESCRIPTIVE INDICES

2019 ◽  
Vol 14 (Number 1) ◽  
pp. 28-42
Author(s):  
Yasir Hilal Hadi ◽  
Ku Ruhana Ku-Mahamud ◽  
Wan Hussain Wan Ishak
Keyword(s):  
Mean Absolute Error ◽  
Back Propagation ◽  
Extreme Rainfall ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Human Environment ◽  
Model Based ◽  
Rainfall Patterns

Extreme rainfall is one of the disastrous events that occurred due to massive rainfall overcometime beyond the regularrainfall rate. The catastrophic effects of extreme rainfall on human, environment, and economy are enormous as most of the events are unpredictable. Modelling the extreme rainfall patterns is a challenge since the extreme rainfall patterns are infrequent.In this study, a model based on descriptive indices to forecast extreme rainfall is proposed. The indices that are calculated every monthare used to develop a Back Propagation Neural Network model in forecasting extreme rainfall. Experiments were conducted using different combinations of indices and results were compared with actual data based on mean absolute error. The results showed that the combination of six indices achieved the best performance,and this proved that indices couldbe used for forecasting extreme rainfall values.

Modeling and Predicting Tensile Strength of Tungsten Alloy by Using PSO-SVR

2012 ◽  
Vol 455-456 ◽  
pp. 1497-1503
Author(s):  
J.L. Tang ◽  
C.Z. Cai ◽  
X.J. Zhu ◽  
G.L. Wang ◽  
D.F. Cao
Keyword(s):  
Tensile Strength ◽  
Mean Absolute Error ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Influential Factors ◽  
Tungsten Alloy ◽  
Percentage Error ◽  
The Mean ◽  
Bpnn Model

In this paper, the support vecstor regression (SVR) approach combined with particle swarm optimization (PSO) is proposed to establish a model for predicting tungsten tensile strength base on the tension experimental data of tungsten alloy under two influential factors, including tungsten content and deformation magnitude. Comparing the prediction result of PSO-SVR model with that of back propagation neural network (BPNN) model, it is shown that the prediction precision of SVR model is higher evaluated by identical training and test samples. The mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of SVR model, all are smaller than those of BPNN. This study suggests that SVR is an effective and powerful tool for predicting the tensile strength of tungsten alloy.

Sign in / Sign up

Export Citation Format

Share Document