Comparison of neuro-fuzzy and neural networks techniques for estimating ammonia concentration in poultry farms

Abstract Ammonia (NH3) is a primary air pollutant in poultry farms that affects the ecosystem, environment, and birds and humans' health adversely. Therefore, estimating NH3 concentration is valuable in research on environmental protection, human and animal health, litter management, etc. The study's main objective was to develop a simple, accurate, rapid, and economic model that estimates NH3 concentration in poultry farms best. To do so, four different models—multilayer perceptron (MLP), integrated adaptive neuro-fuzzy inference systems with grid partitioning and subtractive clustering (ANFIS-GP and ANFIS-SC), and multiple linear regression analysis (MLR)—were performed to estimate NH3 concentration in poultry farms using climatic variables and litter quality properties that can be obtained easily. The root mean square error (RMSE), mean relative percentage absolute error (MRPE), and determination coefficient (R2) were used to evaluate the applied models' performance. A comparison of the results indicated that the ANFIS-SC model, the inputs of which are air temperature, air relative humidity, and airspeed, was the most suitable estimation model with respect to RMSE, MRPE, and R2 to predict NH3 concentration (1.130 ppm, 4.032%, and 0.858, respectively) for the validation dataset. The MLR model's results were the least accurate. In conclusion, this study recommends the neurocomputing model developed as an alternative approach to estimating NH3 concentration in poultry farms because it yields accurate estimations quickly.

[1]  Jan Dimon Bendtsen,et al.  Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models , 2017 .

[2]  Cheng-Jian Lin,et al.  Fuzzy adaptive learning control network with on-line neural learning , 1995 .

[3]  Bahman Najafi,et al.  Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC) , 2018, Resources, Conservation and Recycling.

[4]  Ji-Qin Ni,et al.  Mechanistic Models of Ammonia Release from Liquid Manure: a Review , 1999 .

[5]  M. Cobaner.  Evapotranspiration estimation by two different neuro-fuzzy inference systems , 2011 .

[6]  Artificial-Intelligence Technology Predicts Relative Permeability of Giant Carbonate Reservoirs , 2009 .

[7]  Yu-Cheng Tang,et al.  An approach to budget allocation for an aerospace company - Fuzzy analytic hierarchy process and artificial neural network , 2009, Neurocomputing.

[8]  H. Xin,et al.  Ammonia Concentrations and Emissions in Livestock Production Facilities: Guidelines and Limits in the USA and UK , 2003 .

[9]  S. Abdanan Mehdizadeh,et al.  Broiler weight estimation based on machine vision and artificial neural network , 2017, British poultry science.

[10]  A. Arabi Yazdi,et al.  Flood flow forecasting using ANN, ANFIS and regression models , 2013, Neural Computing and Applications.

[11]  R. Teeter,et al.  Atmospheric Ammonia Concentration Effects on Broiler Growth and Performance , 2004 .

[12]  Bilal Cemek,et al.  Leaf area modeling of bell pepper (Capsicum annuum L.) grown under different stress conditions by soft computing approaches , 2020, Comput. Electron. Agric..

[13]  Saeed Tavakoli,et al.  Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran , 2015 .

[14]  S. Capareda,et al.  Application of artificial neural network in the modeling and optimization of humic acid extraction from municipal solid waste biochar , 2017 .

[15]  H. Ahmadi,et al.  A Gaussian process regression model to predict energy contents of corn for poultry , 2020, Poultry science.

[16]  R. Bozorgmehry Boozarjomehry,et al.  Estimation of PC-SAFT binary interaction coefficient by artificial neural network for multicomponent phase equilibrium calculations , 2020 .

[17]  H. H. Kristensen,et al.  Ammonia and poultry welfare: a review , 2000 .

[18]  Özgür Kisi,et al.  Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff , 2013, Appl. Soft Comput..

[19]  Miquel Sànchez-Marrè,et al.  Designing and building real environmental decision support systems , 2004, Environ. Model. Softw..

[20]  D. Miles,et al.  Atmospheric ammonia is detrimental to the performance of modern commercial broilers. , 2004, Poultry science.

[21]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[22]  Aalap Doshi,et al.  A comprehensive review on automation in agriculture using artificial intelligence , 2019, Artificial Intelligence in Agriculture.

[23]  Andreas Ihrig,et al.  Examination of the influence of personal traits and habituation on the reporting of complaints at experimental exposure to ammonia , 2006, International archives of occupational and environmental health.

[24]  Yi-Chung Hu,et al.  Sugeno fuzzy integral for finding fuzzy if-then classification rules , 2007, Appl. Math. Comput..

[25]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[26]  Subana Shanmuganathan,et al.  Artificial Neural Network Modelling: An Introduction , 2016 .

[27]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[28]  Holger R. Maier,et al.  ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING , 2001 .

[29]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[30]  W B Roush,et al.  Comparison of Gompertz and neural network models of broiler growth. , 2006, Poultry science.

[31]  B. Zaffer,et al.  Ammonia production in the poultry houses and its harmful effects , 2018 .

[32]  Mohamed Sherif Zaghloul,et al.  Comparison of adaptive neuro-fuzzy inference systems (ANFIS) and support vector regression (SVR) for data-driven modelling of aerobic granular sludge reactors , 2020, Journal of Environmental Chemical Engineering.

[33]  N. Güler,et al.  A Study on Multiple Linear Regression Analysis , 2013 .

[34]  Ozgur Kisi,et al.  Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data , 2005 .

[35]  M. Y. El-Bakry.  Feed forward neural networks modeling for K-P interactions , 2003 .

[36]  S. Naseem,et al.  Ammonia production in poultry houses can affect health of humans, birds, and the environment—techniques for its reduction during poultry production , 2018, Environmental Science and Pollution Research.

[37]  Ozgur Kisi,et al.  Dissolved oxygen prediction using a new ensemble method , 2020, Environmental Science and Pollution Research.

[38]  Se-Woon Hong,et al.  CFD modeling of airflow, thermal environment, and ammonia concentration distribution in a commercial manure-belt layer house with mixed ventilation systems , 2019, Comput. Electron. Agric..

[39]  Hongwei Xin,et al.  Method for Measuring Ammonia Emissions from Poultry Houses , 2005 .

[40]  E. Almuhanna,et al.  Effect of Air Contaminants on Poultry Immunological and Production Performance , 2011 .

[41]  J. Robertson,et al.  Review of the effect of ammonia and dust concentrations on broiler performance , 2003 .

[42]  Hong Li,et al.  Assessment of frequent litter amendment application on ammonia emission from broilers operations , 2013, Journal of the Air & Waste Management Association.

[43]  Jamshid Piri,et al.  Application of ANN and ANFIS models for reconstructing missing flow data , 2010, Environmental monitoring and assessment.

[44]  Marcelo Teixeira,et al.  Generating action plans for poultry management using artificial neural networks , 2019, Comput. Electron. Agric..

[45]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[46]  Roland T. Rust,et al.  Artificial Intelligence in Service , 2018 .

[47]  Abbas Rohani,et al.  Applied improved RBF neural network model for predicting the broiler output energies , 2020, Appl. Soft Comput..

[48]  L. Jones,et al.  Upwind impacts of ammonia from an intensive poultry unit. , 2013, Environmental pollution.

[49]  Hongwei Xin,et al.  Ammonia Emissions from U.S. Laying Hen Houses in Iowa and Pennsylvania , 2005 .

[50]  Jan Adamowski,et al.  Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms , 2010 .

[51]  Mansour Talebizadeh,et al.  Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models , 2011, Expert Syst. Appl..

[52]  G. K. Nkonde,et al.  Ammonia Production In Poultry Houses And Its Effect On The Growth Of Gallus Gallus Domestica (Broiler Chickens): A Case Study Of A Small Scale Poultry House In Riverside, Kitwe, Zambia , 2015 .