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Neural Network training model for weather forecasting using Fireworks Algorithm

机译:使用Fireworks算法的天气预报神经网络训练模型

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Weather forecasting is the application of science and technology in order to predict the weather conditions. It is important for agricultural and industrial sectors. Models of Artificial Neural Networks with supervised learning paradigm are suitable for weather forecasting in complexity atmosphere. Training algorithm is required for providing weight and bias values to the model. This research proposed a weather forecasting method using Artificial Neural Networks trained by Fireworks Algorithm. Fireworks Algorithm is a recently developed Swarm Intelligence Algorithm for optimization. The main objective of the method is to predict daily mean temperature based on various measured parameters gained from the Meteorological Station, located in Bangkok. The experimental results indicate that the proposed method is advantageous for weather forecasting.
机译:天气预报是为了预测天气状况而应用的科学技术。这对农业和工业部门很重要。具有监督学习范式的人工神经网络模型适用于复杂大气中的天气预报。需要训练算法来为模型提供权重和偏差值。这项研究提出了一种使用Fireworks算法训练的人工神经网络进行天气预报的方法。 Fireworks Algorithm是最近开发的用于优化的Swarm Intelligence算法。该方法的主要目的是根据从位于曼谷的气象站获得的各种测量参数来预测日平均温度。实验结果表明,该方法对天气预报具有优势。

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