ResearchArticle ArtificialNeuralNetworktoEstimatethePaddyYieldPrediction UsingClimaticData VinushiAmaratunga, 1 LasiniWickramasinghe, 2 AnushkaPerera, 1 JeevaniJayasinghe, 2 andUpakaRathnayake 1 1 Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka 2 Department of Electronics, Faculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, Sri Lanka Correspondence should be addressed to Upaka Rathnayake; upakasanjeewa@gmail.com Received 7 May 2020; Revised 23 June 2020; Accepted 30 June 2020; Published 18 July 2020 Academic Editor: Jian G. Zhou Copyright © 2020 Vinushi Amaratunga et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. erefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. erefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. ree training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. e results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. e ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models. 1.Introduction Rice is the staple food of almost all Sri Lankans. erefore, it is estimated that 2.7 million metric tons of rough rice (paddy) is produced annually to satisfy the demand (around 95%) of the country [1]. More than 1.8 million farmers and farming families involve in this production. erefore, it is important none other than any other agricultural products in Sri Lanka. However, paddy, as a crop, is one of the most affected cultivations in many countries due to the on-going climate variability [2, 3]. is is mainly because of the water requirement for the paddy cultivation. With increasing global temperatures, resulting devia- tions in rainfall patterns cause immense impact on the crop growth. us, the water availability for crops es- sentially depends upon rainfall distribution. Moreover, intense and excess rainfall can produce adverse effects, along with major flooding devastating vegetation, while crop yield also reduces due to water shortage in drought climates. Nevertheless, rice cultivation is considered a Hindawi Mathematical Problems in Engineering Volume 2020, Article ID 8627824, 11 pages https://doi.org/10.1155/2020/8627824