scholarly journals Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration

2021 ◽  
Vol 11 (18) ◽  
pp. 8649
Author(s):  
Yan Liu ◽  
Sha Zhang ◽  
Jiahua Zhang ◽  
Lili Tang ◽  
Yun Bai
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Agricultural Development ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Physical Models ◽  
Accurate Estimation ◽  
Surface Conductance ◽  
Artificial Neural

Accurate estimation of evapotranspiration (ET) can provide useful information for water management and sustainable agricultural development. However, most of the existing studies used physical models, which are not accurate enough due to our limited ability to represent the ET process accurately or rarely focused on cropland. In this study, we trained two models of estimating croplands ET. The first is Medlyn–Penman–Monteith (Medlyn–PM) model. It uses artificial neural network (ANN)-derived gross primary production along with Medlyn’s stomatal conductance to compute surface conductance (Gs), and the computed Gs is used to estimate ET using the PM equation. The second model, termed ANN-PM, directly uses ANN to construct Gs and simulate ET using the PM equation. The results showed that the two models can reasonably reproduce ET with ANN-PM showing a better performance, as indicated by the lower error and higher determination coefficients. The results also showed that the performances of ANN-PM without the facilitation of any remote sensing (RS) factors degraded significantly compared to the versions that used RS factors. We also evidenced that ANN-PM can reasonably characterize the time-series changes of ET at sites having a dry climate. The ANN-PM method can reasonably estimate the ET of croplands under different environmental conditions.

scholarly journals RUNOFF ESTIMATION IN URBAN CATCHMENT USING ARTIFICIAL NEURAL NETWORK MODELS

2020 ◽  
Vol 21 (Supplement 1) ◽  
Author(s):  
Baharak Motamedvaziri ◽  
Baharak Motamedvaziri ◽  
Baharak Motamedvaziri ◽  
Payam Najafi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Input Vector ◽  
Physical Models ◽  
Accurate Estimation ◽  
Hydrological Process ◽  
Neural Network Models ◽  
Study Results ◽  
Artificial Neural

Many types of physical models have been developed for runoff estimation with successful results. However, accurate estimation of runoff remains a challenging problem owing to the lack of field data and the complexity of its hydrological process. In this paper, a machine learning method for runoff estimation is presented as an alternative approach to the physical model. Various types of input variables and artificial neural network (ANN) architectures were examined in this study. Results showed that a two-layer network with the tansig activation function and the Levenberg–Marquardt learning algorithm performed the best. For this architecture, the most effective input vector consists of a catchment perimeter, canal length, slope, runoff coefficient, and rainfall intensity. However, results of multivariate analysis of variance indicated the significant interaction effect of input data and the ANN architecture. Thus, to create a suitable ANN model for runoff estimation, a systematic determination of the input vector is necessary

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.

scholarly journals Artificial Neural Network Simulation of Energetic Performance for Sorption Thermal Energy Storage Reactors

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3294
Author(s):  
Carla Delmarre ◽  
Marie-Anne Resmond ◽  
Frédéric Kuznik ◽  
Christian Obrecht ◽  
Bao Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Thermal Energy ◽  
Temperature Difference ◽  
Thermal Energy Storage ◽  
Heat Storage ◽  
Experimental Results ◽  
Physical Models ◽  
Artificial Neural

Sorption thermal heat storage is a promising solution to improve the development of renewable energies and to promote a rational use of energy both for industry and households. These systems store thermal energy through physico-chemical sorption/desorption reactions that are also termed hydration/dehydration. Their introduction to the market requires to assess their energy performances, usually analysed by numerical simulation of the overall system. To address this, physical models are commonly developed and used. However, simulation based on such models are time-consuming which does not allow their use for yearly simulations. Artificial neural network (ANN)-based models, which are known for their computational efficiency, may overcome this issue. Therefore, the main objective of this study is to investigate the use of an ANN model to simulate a sorption heat storage system, instead of using a physical model. The neural network is trained using experimental results in order to evaluate this approach on actual systems. By using a recurrent neural network (RNN) and the Deep Learning Toolbox in MATLAB, a good accuracy is reached, and the predicted results are close to the experimental results. The root mean squared error for the prediction of the temperature difference during the thermal energy storage process is less than 3K for both hydration and dehydration, the maximal temperature difference being, respectively, about 90K and 40K.

scholarly journals Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 134-143 ◽  
Author(s):  
Tuan Nguyen Thanh ◽  
Tai Dinh Tien ◽  
Hai Long Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Northeast China ◽  
Tree Height ◽  
Pinus Koraiensis ◽  
Accurate Estimation ◽  
Korean Pine ◽  
Ann Models ◽  
Artificial Neural ◽  
Forest Farm

Korean pine (Pinus koraiensis Sieb. et Zucc.) is one of the highly commercial woody species in Northeast China. In this study, six nonlinear equations and artificial neural network (ANN) models were employed to model and validate height-diameter (H-DBH) relationship in three different stand densities of one Korean pine plantation. Data were collected in 12 plots in a 43-year-old even-aged stand of P. koraiensis in Mengjiagang Forest Farm, China. The data were randomly split into two datasets for model development (9 plots) and for model validation (3 plots). All candidate models showed a good perfomance in explaining H-DBH relationship with error estimation of tree height ranging from 0.61 to 1.52 m. Especially, ANN models could reduce the root mean square error (RMSE) by the highest 40%, compared with Power function for the density level of 600 trees. In general, our results showed that ANN models were superior to other six nonlinear models. The H-DBH relationship appeared to differ between stand density levels, thus it is necessary to establish H-DBH models for specific stand densities to provide more accurate estimation of tree height.

Artificial Neural Network Model for Diagnosing the Performance and the Conditions of Air-Operated Valves

2020 ◽  
Author(s):  
Taeyun Kim ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Ilyoung Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Physical Models ◽  
Artificial Neural

Abstract Air-operated valves (AOVs) are used to control or shut off the flow in the nuclear power plants. In particular, the failure of safety-related AOV could have significant impacts on the safety of the nuclear power plants and therefore, their performances have been tested and evaluated periodically. However, the current method to evaluate the performance needs to be revised to enhance the accuracy and to identify defects of AOV independently of personal skills. This paper introduce the ANN (Artificial Neural Network) model to diagnose the performance and the condition altogether. Test facilities were designed and configured to measure the signals such as supply pressure, control pressure, actuator pressure, stem displacement and stem thrust. Tests were carried out in various conditions which simulate defects with leak/clogged pipes, the bent stem and so on. First, the physical models of an AOV are developed to describe its behavior and to parameterize the characteristics of each component for evaluating the performance. Secondly, CNN (Convolutional Neural Network) architectures are designed considering the developed physical models to make a lead to the optimal performance of ANN. To train the ANN effectively, the measured signals were divided into several regions, from each of which the features are extracted and the extracted features are combined for classifying the defects. In addition, the model can provide the parameters of maximum available thrust, which is the key factor in periodic verification of AOV with the required accuracy and classify more than 10 different kinds of defects with high accuracy.

Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques

2019 ◽  
Vol 20 (3) ◽  
pp. 800-808
Author(s):  
G. T. Patle ◽  
M. Chettri ◽  
D. Jhajharia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Relative Humidity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Accurate Estimation ◽  
Pan Evaporation ◽  
Ann Models ◽  
Artificial Neural

Abstract Accurate estimation of evaporation from agricultural fields and water bodies is needed for the efficient utilisation and management of water resources at the watershed and regional scale. In this study, multiple linear regression (MLR) and artificial neural network (ANN) techniques are used for the estimation of monthly pan evaporation. The modelling approach includes the various combination of six measured climate parameters consisting of maximum and minimum air temperature, maximum and minimum relative humidity, sunshine hours and wind speed of two stations, namely Gangtok in Sikkim and Imphal in the Manipur states of the northeast hill region of India. Average monthly evaporation varies from 0.62 to 2.68 mm/day for Gangtok, whereas it varies from 1.4 to 4.3 mm/day for Imphal during January and June, respectively. Performance of the developed MLR and ANN models was compared using statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) with measured pan evaporation values. Correlation analysis revealed that temperature, wind speed and sunshine hour had positive correlation, whereas relative humidity had a negative correlation with pan evaporation. Results showed a slightly better performance of the ANN models over the MLR models for the prediction of monthly pan evaporation in the study area.

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