1. Prediction of Wedelia trilobata Growth under Flooding and Nitrogen Enrichment Conditions by Using Artificial Neural Network Model.
- Author
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Azeem, Ahmad, Mai Wenxuan, Tian Changyan, Qamar, Muhammad Uzair, and Buttar, Noman Ali
- Subjects
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MACHINE learning , *STANDARD deviations , *INVASIVE plants , *ARTIFICIAL neural networks - Abstract
The objective of this study is to produce multi-criteria model for the dry weight prediction of Wedelia trilobata under flooding and nitrogen conditions. Plants of W. trilobata were grown in a greenhouse, and treatments were given for two months. Growth parameters of 60 plants were used to build a numerical model. The neural network model was built using Quasi-Newton approaches that containing Broyden-fletcher-goldfarb-shanno gradient (BFGS) learning algorithm, multilayer perceptron (MLP) training algorithm and sigmoid axon transfer function along with 10 neurons at the input network, 9 neurons in the hidden layer, and 1 neuron in the output layer (10-9-1). The selection and validation of the best predictor model were based on lower values of errors and higher value of R². The selected model had a higher values of R² = 0.90 and lower values of errors i.e (relative approximate error, RAE = 0.004, root mean square error, RMS = 0.027, mean absolute error, MAE = 0.004, mean absolute percentage error, MAPE = 0.013). Moreover, the highest rank 1 was obtained for leaf area during sensitivity analysis followed by water potential and photosynthesis ranked 2rd and 3th, respectively. The constructed model of W. trilobata under flooding and nitrogen conditions is the new feature in the management of invasive plant species and gives direction to control its spread. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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