利用长短期记忆(LSTM)预测茂物市降雨量参数

Sherly Amora Jofipasi, Admi Salma, Dodi Vionanda, Dina Fitria
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引用次数: 0

摘要

茂物是一个降雨量大且不稳定的城市。因此有必要预测茂物的降雨量。降雨预测可以使用 LSTM 算法。在 LSTM 算法中,需要隐神经元参数和历时来产生良好的结果,因此有必要预测茂物降雨的最佳参数。使用最佳隐藏神经元值 256、最佳历时 150、mape 1,64 时,LSTM 得出的预测参数结果效果良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction Of Bogor City Rainfall Parameters Using Long Short Term Memory (LSTM)
Bogor is a city that has high rainfall and has erratic rainfall. So it is necessary to predict Bogor's rainfall. Rainfall prediction can be done using the LSTM algorith. In the LSTM algorithm, there are hidden neuron parameters and epochs to produce good results, so it is necessary to predict the best parameters in Bogor rainfall. The prediction parameters results obtained by LSTM have worked well using optimal hidden neuron values of 256, optimal epoch of 150, mape of 1,64, and the comparison of actual data patterns and prediction data already has the same data patterns.
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