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 Drought prediction based on an improved VMD-OS-QR-ELM model

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262329
Author(s):  
Yang Liu ◽  
Li Hu Wang ◽  
Li Bo Yang ◽  
Xue Mei Liu
Keyword(s):  
Prediction Models ◽  
Prediction Method ◽  
Triangular Matrix ◽  
Matrix Decomposition ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Drought Prediction ◽  
Mode Decomposition ◽  
Learning Machine ◽  
Hidden Layer

To overcome the low accuracy, poor reliability, and delay in the current drought prediction models, we propose a new extreme learning machine (ELM) based on an improved variational mode decomposition (VMD). The model first redefines the output of the hidden layer of the ELM model with orthogonal triangular matrix decomposition (QR) to construct an orthogonal triangular ELM (QR-ELM), and then introduces an online sequence learning mechanism (OS) into the QR-ELM to construct an online sequence OR-ELM (OS-QR-ELM), which effectively improves the efficiency of the ELM model. The mutual information extension method was then used to extend both ends of the original signal to improve the VMD end effect. Finally, VMD and OS-QR-ELM were combined to construct a drought prediction method based on the VMD-OS-QR-ELM. The reliability and accuracy of the VMD-OS-QR-ELM model were improved by 86.19% and 93.20%, respectively, compared with those of the support vector regression model combined with empirical mode decomposition. Furthermore, the calculation efficiency of the OS-QR-ELM model was increased by 88.65% and 85.32% compared with that of the ELM and QR-ELM models, respectively.

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

2020 ◽  
pp. 095440542097106
Author(s):  
Yu Su ◽  
Congbo Li ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Guangxi Zhao
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Tool Wear ◽  
Specific Energy ◽  
Machine Tools ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Support Vector

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

A Traffic Prediction Method Based on the Hybrid Model of CLWT

2015 ◽  
Vol 713-715 ◽  
pp. 1564-1569
Author(s):  
Jin Long Fei ◽  
Wei Lin ◽  
Tao Han ◽  
Yue Fei Zhu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Recurrent Nerve ◽  
Nerve Network ◽  
Traffic Time Series

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

scholarly journals Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises

2016 ◽  
Vol 16 (6) ◽  
pp. 83-97 ◽  
Author(s):  
Rui-Dong Wang ◽  
Xue-Shan Sun ◽  
Xin Yang ◽  
Haiju Hu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Distributed Energy ◽  
Manufacturing Enterprises ◽  
High Dimension Data ◽  
Equipment Manufacturing ◽  
Neural Network Algorithm ◽  
Learning Machine

Abstract Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce energy consumption. Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine considering the practical enterprise situation of massive and high-dimension data. The analysis of the real energy consumption data provided by LB Enterprise was undertaken and corresponding calculating experiments were completed using a 32-node cloud computing cluster. The experimental results show that the energy consumption forecasting accuracy of the proposed model is higher than the traditional support vector regression and the generalized neural network algorithm. Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer’s insufficient computing power when facing massive and high-dimensional data without increasing the cost.

scholarly journals A Novel Machine Learning Based Method of Combined Dynamic Environment Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Wentao Mao ◽  
Guirong Yan ◽  
Longlei Dong
Keyword(s):  
Machine Learning ◽  
Multiple Input Multiple Output ◽  
Dynamic Environment ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Structural Vibration ◽  
Support Vector ◽  
Prediction Errors ◽  
Vibration Responses ◽  
Learning Machine

In practical engineerings, structures are often excited by different kinds of loads at the same time. How to effectively analyze and simulate this kind of dynamic environment of structure, named combined dynamic environment, is one of the key issues. In this paper, a novel prediction method of combined dynamic environment is proposed from the perspective of data analysis. First, the existence of dynamic similarity between vibration responses of the same structure under different boundary conditions is theoretically proven. It is further proven that this similarity can be established by a multiple-input multiple-output regression model. Second, two machine learning algorithms, multiple-dimensional support vector machine and extreme learning machine, are introduced to establish this model. To test the effectiveness of this method, shock and stochastic white noise excitations are acted on a cylindrical shell with two clamps to simulate different dynamic environments. The prediction errors on various measuring points are all less than ±3 dB, which shows that the proposed method can predict the structural vibration response under one boundary condition by means of the response under another condition in terms of precision and numerical stability.

scholarly journals Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

2020 ◽  
Vol 309 ◽  
pp. 04018
Author(s):  
Guangjie Hao ◽  
Menghong Yu ◽  
Zhen Su
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Prediction Method ◽  
Bat Algorithm ◽  
Yangtze Estuary ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Density Prediction ◽  
Learning Machine ◽  
Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

scholarly journals Prediction of scour depth around bridge piers using self-adaptive extreme learning machine

2016 ◽  
Vol 19 (2) ◽  
pp. 207-224 ◽  
Author(s):  
Isa Ebtehaj ◽  
Ahmed M. A. Sattar ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Differential Evolution Algorithm ◽  
Bridge Piers ◽  
Support Vector ◽  
Scour Depth ◽  
Pier Scour ◽  
New Model ◽  
Learning Machine ◽  
Self Adaptive

Accurate prediction of pier scour can lead to economic design of bridge piers and prevent catastrophic incidents. This paper presents the application of self-adaptive evolutionary extreme learning machine (SAELM) to develop a new model for the prediction of local scour around bridge piers using 476 field pier scour measurements with four shapes of piers: sharp, round, cylindrical, and square. The model network parameters are optimized using the differential evolution algorithm. The best SAELM model calculates the scour depth as a function of pier dimensions and the sediment mean diameter. The developed SAELM model had the lowest error indicators when compared to regression-based prediction models for root mean square error (RMSE) (0.15, 0.65, respectively) and mean absolute relative error (MARE) (0.50, 2.0, respectively). The SAELM model was found to perform better than artificial neural networks or support vector machines on the same dataset. Parametric analysis showed that the new model predictions are influenced by pier dimensions and bed-sediment size and produce similar trends of variations of scour-hole depth as reported in literature and previous experimental measurements. The prediction uncertainty of the developed SAELM model is quantified and compared with existing regression-based models and found to be the least, ±0.03 compared with ±0.10 for other models.

IMPROVED ITERATIVE PREDICTION FOR MULTIPLE STOP ARRIVAL TIME USING A SUPPORT VECTOR MACHINE

Transport ◽  
2012 ◽  
Vol 27 (2) ◽  
pp. 158-164 ◽  
Author(s):  
Chang-Jiang Zheng ◽  
Yi-Hua Zhang ◽  
Xue-Jun Feng
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Arrival Time ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Dynamic Algorithm ◽  
Proposed Model ◽  
Speed Prediction ◽  
Two Stages

The paper presents an improved iterative prediction method for bus arrival time at multiple downstream stops. A multiple-stop prediction model includes two stages. At the first stage, an iterative prediction model is developed, which includes a single stop prediction model for arrival time at the immediate downstream stop and an average bus speed prediction model on further segments. The two prediction models are constructed with a support vector machine (SVM). At the second stage, a dynamic algorithm based on the Kalman filter is developed to enhance prediction accuracy. The proposed model is assessed with reference to data collected on transit route No 23 in Dalian city, China. The obtained results show that the improved iterative prediction model seems to be a powerful tool for predicting multiple stop arrival time.

scholarly journals Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zhenhua Yang ◽  
Hongwei Zhang ◽  
Sheng Li ◽  
Chaojun Fan
Keyword(s):  
Coal Seam ◽  
Extreme Learning Machine ◽  
Back Propagation ◽  
Prediction Method ◽  
Gas Content ◽  
Percentage Error ◽  
Support Vector ◽  
Prevention Measures ◽  
Residual Gas ◽  
Learning Machine

In order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevention measures (drilling cuttings indices, residual gas content in coal seam) during roadway advancing in Yuecheng coal mine were first analyzed. Then, the correlation between drilling cuttings indices and residual gas content was established, as well as the neural network prediction model based on BA-ELM. Finally, the prediction result of the proposed method was compared with that of back-propagation (BP), support vector machine (SVM), and extreme learning machine (ELM) to verify the accuracy. The results show that the average absolute error, the average absolute percentage error, and the determination coefficient of the proposed prediction method of residual gas content in coal seam are 0.069, 0.012, and 0.981, respectively. This method has higher accuracy than other methods and can effectively reveal the nonlinear relationship between drilling cuttings indices and residual gas content. It has prospective application in the prediction of residual gas content in coal seam.

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