Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

Recent advancements of computer and electronic systems have motivated the extensive use of intelligent systems for automation of agricultural industries. In this study, the temperature variation of the mushroom growing room is modeled through using a multi-layered perceptron (MLP) and radial basis function networks. Modeling has been done based on the independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP is found to be the second repetition with 12 neurons in the hidden layer and 20 neurons in the hidden layer for radial basis function networks. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural networks with radial basis function was selected as the optimal predictor for the behavior of the system.

[1]  Cécile Bothorel,et al.  Big Data - State of the Art , 2013 .

[2]  Amir Mosavi,et al.  Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model , 2019, Infrastructures.

[3]  Annamária R. Várkonyi-Kóczy,et al.  Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology , 2019 .

[4]  Annamária R. Várkonyi-Kóczy,et al.  Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content , 2019, Computers, Materials & Continua.

[5]  Amir Mosavi,et al.  Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road) , 2019, Engineering Applications of Computational Fluid Mechanics.

[6]  Annamária R. Várkonyi-Kóczy,et al.  Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review , 2019 .

[7]  Amir Mosavi,et al.  Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases , 2019, Mathematics.

[8]  Annamária R. Várkonyi-Kóczy,et al.  A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation , 2018, Recent Advances in Technology Research and Education.

[9]  Annamária R. Várkonyi-Kóczy,et al.  Reviewing the novel machine learning tools for materials design , 2017 .

[10]  Annamária R. Várkonyi-Kóczy,et al.  Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks , 2019, Lecture Notes in Networks and Systems.

[11]  Annamária R. Várkonyi-Kóczy,et al.  List of Deep Learning Models , 2019, Lecture Notes in Networks and Systems.

[12]  Amir Mosavi,et al.  Numerical simulation of pressure pulsation effects of a snubber in a CNG station for increasing measurement accuracy , 2019, Engineering Applications of Computational Fluid Mechanics.

[13]  Hossein Navid,et al.  Application of artificial neural network in prediction of the combine harvester performance , 2010 .

[14]  Shahaboddin Shamshirband,et al.  State of the Art of Machine Learning Models in Energy Systems, a Systematic Review , 2019, Energies.

[15]  Asghar Mahmoudi,et al.  Modeling and comparison of fuzzy and on/off controller in a mushroom growing hall , 2016 .

[16]  Amir Mosavi,et al.  Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer , 2019, Sustainability.

[17]  Amir Mosavi,et al.  Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research , 2019, Lecture Notes in Networks and Systems.

[18]  Annamária R. Várkonyi-Kóczy,et al.  Integration of Machine Learning and Optimization for Robot Learning , 2016 .

[19]  Hossien Riahi-Madvar,et al.  Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry , 2019, Engineering Applications of Computational Fluid Mechanics.

[20]  Amir Mosavi,et al.  Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming , 2019, Engineering Applications of Computational Fluid Mechanics.

[21]  Annamária R. Várkonyi-Kóczy,et al.  Industrial applications of Big Data: State of the art survey , 2017 .

[22]  Annamária R. Várkonyi-Kóczy,et al.  Urban Train Soil-Structure Interaction Modeling and Analysis , 2019, Lecture Notes in Networks and Systems.

[23]  S. Kurashige,et al.  Effects of Lentinus edodes, Grifola frondosa and Pleurotus ostreatus administration on cancer outbreak, and activities of macrophages and lymphocytes in mice treated with a carcinogen, N-butyl-N-butanolnitrosoamine. , 1997, Immunopharmacology and immunotoxicology.

[24]  F. Gong,et al.  Flammulin Purified from the Fruit Bodies of Flammulina velutipes (Curt.:Fr.) P.Karst. , 1999 .

[25]  Amir Mosavi,et al.  State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability , 2019, Lecture Notes in Networks and Systems.

[26]  Asghar Mahmoudi,et al.  Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers , 2016 .

[27]  Annamária R. Várkonyi-Kóczy,et al.  Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research , 2019 .

[28]  Ely Salwana,et al.  Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning , 2019, Engineering Applications of Computational Fluid Mechanics.

[29]  Shahaboddin Shamshirband,et al.  Particle swarm optimization model to predict scour depth around a bridge pier , 2019, Frontiers of Structural and Civil Engineering.

[30]  Amir Mosavi,et al.  ANFIS pattern for molecular membranes separation optimization , 2019, Journal of Molecular Liquids.

[31]  Annamária R. Várkonyi-Kóczy,et al.  Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2 , 2019, Computers, Materials & Continua.

[32]  Shahaboddin Shamshirband,et al.  Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System , 2019, Energies.

[33]  Amir Mosavi,et al.  Groundwater Quality Assessment for Drinking and Agricultural Purposes in Tabriz Aquifer, Iran , 2019 .

[34]  J. Adamowski,et al.  An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. , 2019, The Science of the total environment.

[35]  Amir Mosavi,et al.  Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters , 2018, Engineering Applications of Computational Fluid Mechanics.

[36]  Juntao Fei,et al.  Model reference adaptive sliding mode control using RBF neural network for active power filter , 2015 .

[37]  V. Singh,et al.  Snow avalanche hazard prediction using machine learning methods , 2019, Journal of Hydrology.

[38]  Timon Rabczuk,et al.  Learning and Intelligent Optimization for Material Design Innovation , 2017, LION.

[39]  Kwok-wing Chau,et al.  Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.

[40]  Annamária R. Várkonyi-Kóczy,et al.  Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities , 2020 .

[41]  Shahaboddin Shamshirband,et al.  Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network , 2018 .

[42]  Amir Mosavi,et al.  A Hybrid clustering and classification technique for forecasting short‐term energy consumption , 2018, Environmental Progress & Sustainable Energy.

[43]  Shahaboddin Shamshirband,et al.  Estimating Daily Dew Point Temperature Using Machine Learning Algorithms , 2019, Water.

[44]  Tibor Kmet,et al.  Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression , 2019, ArXiv.

[45]  Annamária R. Várkonyi-Kóczy,et al.  Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods , 2019, Lecture Notes in Networks and Systems.

[46]  Adrienn Dineva,et al.  Multi-label Classification for Fault Diagnosis of Rotating Electrical Machines , 2019, ArXiv.

[47]  Shahaboddin Shamshirband,et al.  Review of Soft Computing Models in Design and Control of Rotating Electrical Machines , 2019, SSRN Electronic Journal.

[48]  E. Zavadskas,et al.  Sustainable Business Models: A Review , 2019, SSRN Electronic Journal.

[49]  Amir Mosavi,et al.  Investigation of submerged structures’ flexibility on sloshing frequency using a boundary element method and finite element analysis , 2019, Engineering Applications of Computational Fluid Mechanics.

[50]  Amir Mosavi,et al.  Flutter speed estimation using presented differential quadrature method formulation , 2019, Engineering Applications of Computational Fluid Mechanics.

[51]  Amir Mosavi,et al.  Deep Learning for Detecting Building Defects Using Convolutional Neural Networks , 2019, Sensors.

[52]  Amir Mosavi,et al.  A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration , 2018, Recent Advances in Technology Research and Education.