Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has\nbeen accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing\nday-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and\npaddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually\ncomplex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex,\nnonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple\nfood is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic\nparameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala,\nand Puttalam) with available data. Three training algorithms (Levenbergâ??Marquardt (LM), Bayesian Regularization (BR), and\nScaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each\nother to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the\nperformance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal\nthat LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic\nfactors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction\nprocess is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the\nprediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The\nANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data\nfrom various climate models.
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