Sistem Prediksi Curah Hujan Bulanan Menggunakan Jaringan Saraf Tiruan Backpropagation

S. Sunardi, A. Yudhana, Ghufron Zaida Muflih
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引用次数: 5

Abstract

Rainfall has important role for human life. Rainfall information can be used in several fields including agriculture. As a benchmark for planting periods, water infiltration management, and irrigation. The resources for calculating rainfall are rainfall gauges, ground-based radars and remote sensing satellites. Wonosobo area’s rainfall type is monsoon, meaning that it has one wet period and one dry period. It has fluctuating varied rainfall every month and the availability of rainfall data is uncertain each year. As a mountainous area, Wonosobo’s agricultural sector is very dominant for their economic. Weather Observation, especially rainfall, is important because it can be used by related parties, especially in the agricultural sector. In addition, to provide rainfall data in areas with no observation stations. This study aims to design and implement a rainfall prediction system by developing the Waterfall Model Development Life Cycle (SDLC) Software and implementing backpropagation artificial neural networks (ANN). System development using the SDLC waterfall model was chosen because it is simple, easy to understand and implement. ANN backpropagation is applied in the prediction system because of its advantage that can be applied to a problem related to prediction. Testing on the system built for training and validation produces training accuracy of 93.92% with validation of 73.04%, indicating that the system can be used and has been running expectedly. The best ANN architecture was obtained on the test with input layer 3, hidden layer 12, and output 1 values, learning rate 0.5 momentum 0.9. From the SSE 0.1 target, the SSE is 0.302868.
降雨对人类的生活有着重要的作用。降雨信息可用于包括农业在内的多个领域。作为种植期、渗水管理和灌溉的基准。计算雨量的资源有雨量计、地面雷达和遥感卫星。沃诺索博地区的降雨类型是季风,这意味着它有一个湿润期和一个干旱期。它每个月的降雨量都是波动的,每年的降雨量数据的可用性是不确定的。作为一个山区,沃诺索博的农业部门在他们的经济中占主导地位。天气观测,特别是降雨,很重要,因为它可以被相关方使用,特别是在农业部门。此外,提供无观测站地区的降雨资料。本研究旨在通过开发瀑布模型开发生命周期(SDLC)软件和反向传播人工神经网络(ANN)来设计和实现降雨预测系统。选择使用SDLC瀑布模型进行系统开发,因为它简单,易于理解和实现。人工神经网络反向传播由于其可应用于预测相关问题的优点而被应用于预测系统中。对建立的训练验证系统进行测试,训练准确率为93.92%,验证率为73.04%,表明该系统可以使用,并已达到预期的运行效果。在输入层为3,隐藏层为12,输出值为1,学习率为0.5,动量为0.9的测试中获得了最佳的ANN架构。从上证指数0.1的目标来看,上证指数为0.302868。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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