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Communications in Soil Science and Plant Analysis Volume 28, 1997 - Issue 6-8
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Original Articles

Using artificial neural networks and regression to predict percentage of applied nitrogen leached under turfgrass

Steven K. Starrett Civil Engineering Department, Kansas State University, Seaton Hall, Manhattan, KS, 66506; Civil Engineering Department, Kansas State University, Seaton Hall, Manhattan, KS, 66506 E-mail: [email protected]
,
Shelli K. Starrett Electrical and Computer Engineering Department, Kansas State University, Manhattan, KS, 66506
&
G. L. Adams Civil Engineering Department, Kansas State University, Seaton Hall, Manhattan, KS, 66506
Pages 497-507 | Published online: 11 Nov 2008

Citations (19)

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StevenK. Starrett, ShelliK. Starrett, Yacoub Najjar, Greg Adams & Judy Hill. (1998) Modeling pesticide leaching from golf courses using artificial neural networks. Communications in Soil Science and Plant Analysis 29:19-20, pages 3093-3106.
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Articles from other publishers (18)

Yao Sun, Moez Farokhnia Hamedani, Giti Javidi, Ehsan Sheybani & Feng Hao. (2022) Examining COVID-19 vaccine attitude using SEM-Artificial Neural Networks approach: a case from Reddit community. Health Promotion International 37:6.
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Shivani S. Kale & Preeti S. Patil. (2019) A Machine Learning Approach to Predict Crop Yield and Success Rate. A Machine Learning Approach to Predict Crop Yield and Success Rate.
Niketa Gandhi & Leisa J. Armstrong. (2016) A review of the application of data mining techniques for decision making in agriculture. A review of the application of data mining techniques for decision making in agriculture.
Niketa Gandhi, Owaiz Petkar & Leisa J. Armstrong. (2016) Rice crop yield prediction using artificial neural networks. Rice crop yield prediction using artificial neural networks.
Leonardo Lopes Bhering, Cosme Damião Cruz, Leonardo de Azevedo Peixoto, Antônio Marcos Rosado, Bruno Galveas Laviola & Moysés Nascimento. (2015) Application of neural networks to predict volume in eucalyptus. Crop Breeding and Applied Biotechnology 15:3, pages 125-131.
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X. R. Cui, M. F. Abbod, Q. Liu, Jiann-Shing Shieh, T. Y. Chao, C. Y. Hsieh & Y. C. Yang. (2010) Ensembled artificial neural networks to predict the fitness score for body composition analysis. The journal of nutrition, health & aging 15:5, pages 341-348.
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Marvellous M. Zhou, Collins A. Kimbeng, Thomas L. Tew, Kenneth A. Gravois & Michael J. Pontif. (2011) Artificial Neural Network Models as a Decision Support Tool for Selection in Sugarcane: A Case Study Using Seedling Populations. Crop Science 51:1, pages 21-31.
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R. Alvarez. (2009) Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. European Journal of Agronomy 30:2, pages 70-77.
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Steven K. Starrett, Shelli K. Starrett, Travis Heier, Yunsheng Su, Denny Tuan & Mark Bandurraga. (2007) Using Artificial Neural Networks to Fill-in Missing Annual Peak Flows. Using Artificial Neural Networks to Fill-in Missing Annual Peak Flows.
Timothy R. Green, Jose D. Salas, Ana Martinez & Robert H. Erskine. (2007) Relating crop yield to topographic attributes using Spatial Analysis Neural Networks and regression. Geoderma 139:1-2, pages 23-37.
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S.F. Saberali, S.A. Sadat Noori, J. Khazaei & A. Hejazi. (2007) Artificial Neural Network Modelling of Common Lambsquarters Biomass Production Response to Corn Population and Planting Pattern. Pakistan Journal of Biological Sciences 10:2, pages 326-334.
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M. Gevrey, S. Lek & T. Oberdorff. 2006. Ecological Informatics. Ecological Informatics 293 306 .
Monisha Kaul, Robert L. Hill & Charles Walthall. (2005) Artificial neural networks for corn and soybean yield prediction. Agricultural Systems 85:1, pages 1-18.
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Muriel Gevrey, Ioannis Dimopoulos & Sovan Lek. (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160:3, pages 249-264.
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M. Gevrey, S. Lek & T. Oberdorff. 2003. Ecological Informatics. Ecological Informatics 233 246 .
Ling Jing Kao & Chih Chou Chiu. (2001) Mining the customer credit by using the neural network model with classification and regression tree approach. Mining the customer credit by using the neural network model with classification and regression tree approach.
PingSun Leung & Liem T Tran. (2000) Predicting shrimp disease occurrence: artificial neural networks vs. logistic regression. Aquaculture 187:1-2, pages 35-49.
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V. Kiran Kumar, Ramesh KV & V. Rakesh. (2022) Optimizing Lstm and Bi-Lstm Models for Multivariate Time Series Prediction and Performance Comparison with Classical Statistical Models. SSRN Electronic Journal.
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