Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models

2018 ◽  
Vol 172 ◽  
pp. 3028-3041 ◽  
Author(s):  
Morteza Taki ◽  
Abbas Rohani ◽  
Farshad Soheili-Fard ◽  
Abbas Abdeshahi
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Output Energy ◽  
Wheat Production

scholarly journals Machine Learning for Prediction of Energy in Wheat Production

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 517
Author(s):  
Ali Mostafaeipour ◽  
Mohammad Bagher Fakhrzad ◽  
Sajad Gharaat ◽  
Mehdi Jahangiri ◽  
Joshuva Arockia Dhanraj ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Models ◽  
Prediction Method ◽  
Output Energy ◽  
Support Vector ◽  
Fars Province ◽  
Wheat Production ◽  
Total Input ◽  
Learning Machine ◽  
Production Period

The global population growth has led to a considerable rise in demand for wheat. Today, the amount of energy consumption in agriculture has also increased due to the need for sufficient food for the growing population. Thus, agricultural policymakers in most countries rely on prediction models to influence food security policies. This research aims to predict and reduce the amount of energy consumption in wheat production. Data were collected from the farms of Estahban city in Fars province of Iran by the Jihad Agricultural Department’s experts for 20 years from 1994 to 2013. In this study, a novel prediction method based on consumed energy in the production period is proposed. The model is developed based on artificial intelligence to forecast the output energy in wheat production and uses extreme learning machine (ELM) and support vector regression (SVR). In the experimental stage, the value of elevation metrics for the EVM and ELM was reported to be equal to 0.000000409 and 0.9531, respectively. Total input energy (consumed) is found to be 1,460,503.1 Mega Joules (MJ), and output energy (produced wheat) is 1,401,011.945 MJ for the Estahban. The result indicates the superiority of the ELM model to enhance the decisions of the agricultural policymakers.

scholarly journals Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2508 ◽  
Author(s):  
Guolong Zhang ◽  
Ping Wang ◽  
Haibing Chen ◽  
Lan Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gaussian Process ◽  
Hybrid Model ◽  
Nearest Neighbor ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Training Dataset ◽  
K Nearest Neighbor ◽  
Positive Effects

This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

scholarly journals Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3261 ◽  
Author(s):  
Solomon Asante-Okyere ◽  
Chuanbo Shen ◽  
Yao Yevenyo Ziggah ◽  
Mercy Moses Rulegeya ◽  
Xiangfeng Zhu
Keyword(s):  
Neural Network ◽  
Gaussian Process ◽  
Covariance Function ◽  
Back Propagation ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Generalized Regression Neural Network ◽  
Model Parameters ◽  
Porosity And Permeability ◽  
Reservoir Porosity

In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.

scholarly journals Energy Prediction of Wheat Production Using Data Mining Technique in Iran

2021 ◽  
Vol 34 (1) ◽  
pp. 14-27
Author(s):  
Nasim Monjezi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Energy Consumption ◽  
Training Data ◽  
Data Mining Technique ◽  
Essential Information ◽  
Wheat Production ◽  
Energy Prediction ◽  
Mining Technique ◽  
Practical Algorithms

Wheat is considered as one of the most important products in Iran. Concerning high cultivation area of wheat in Khuzestan, an instrument is required to process stored data in order to give information resulted from such processing to managers of agricultural sectors. Data mining technique is able to give essential information and models to producers of wheat for modelling energy consumption. One of the most practical algorithms is an artificial neural network. The main aim of this research is to predict output energy of wheat farms using a multilayer perceptron neural network. This is an analytic research and its database consists of 1240 records. Data required for the research was obtained from wheat farm during 2014-2018. Data analysis was done via IBM SPSS modeller 14.2 and standard CRISP. Concerning the model used in the research, it was found that variables of chemical fertilizers, machinery & diesel fuel with coefficients of 0.2987, 0.2064 and 0.1527 respectively had the highest effect on output variable (productive energy). Amount of prediction precision in neural network algorithm, meaning ratio of correctly predicted records to total records was 93.08%. Also, linear correlation between actual values and predicted values were 0.92 and 0.88 respectively, for training data and testing data suggesting strong correlation.  The results obtained can be effective for wheat farmers in direction of evaluation and optimization of energy consumption in process of wheat production and reduction of consumption of energy inputs.

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