Growth prediction of Alternanthera philoxeroides under salt stress by application of artificial neural networking

Abstract

The purpose of this study was to develop an independent multi-criteria model to predict the growth of invasive Alternanthera philoxeroides under salt stress. Artificial neural-networks with Multi-Layer Perceptron (MLP) were used for building a Predicted Neural Model (PNM) using soil parameters such as pH, electrical conductivity (EC), water content, temperature, humidity, and organic content and a growth parameter, i.e. plant height. Quality assessment of the produced PNM is done through ex-post errors, i.e. Relative-Approximation Error (RAE), Root-Mean Square (RMS) error, Mean-Absolute Error (MAE), and Mean-Absolute Percentage Error (MAPE). The MAPE was 2.21% for PNM of A. philoxeroides, which was less than 10%, thus proving that all the obtained results are highly satisfactory. In the next step, the sensitivity analysis assigned the highest rank 1 to salt stress in the model with a quotient value of 1.71, and the rank-2 was assigned to EC of soil with quotient value of 1.51. Therefore, the constructed PNM will provide the basis for building new prediction tools for the growth of invasive species. It will be an important element for prediction of invasiveness of A. philoxeroides in a stressful environment and will also be helpful for the management of invasive species.