Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting
Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting
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DOI：
10.1016/j.mlwa.2021.100204
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发表时间：
2022-03-15
影响因子：
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通讯作者：
Akanbi, Lukman Adewale
中科院分区：
文献类型：
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作者：
Barrera-Animas, Ari Yair;Oyedele, Lukumon O.;Akanbi, Lukman Adewale
降雨预报由于其复杂性和持久的应用，如洪水预报和监测污染物浓度水平等，近年来获得了最大的研究相关性。现有的模型使用复杂的统计模型，这些模型在计算和预算方面往往过于昂贵，或者不适用于下游应用。因此，正在探索将机器学习算法与时间序列数据结合使用的方法，作为克服这些缺点的替代方案。为此，本研究使用基于传统机器学习算法和深度学习架构的简化降雨量估计模型进行了比较分析，这些模型对这些下游应用程序是有效的。在使用时间序列数据预测每小时降雨量的任务中，比较了基于LSTM、Stacked-LSTM、Bidirectional-LSTM Networks、XGBoost以及梯度提升回归、线性支持向量回归和额外树回归的集合的模型。使用了英国五个主要城市2000年至2020年的气候数据。使用损失、均方根误差、平均绝对误差和均方根对数误差的评估指标来评估模型的性能。结果表明，双向LSTM网络可以用作降雨预报模型，其性能与Stacked-LSTM网络相当。在测试的所有模型中，具有两个隐藏层的StackedLSTM网络和Bidirectional-LSTM网络表现最好。这表明基于具有较少隐藏层的LSTM网络的模型对于这种方法表现更好;表示其能够作为预算明智的降雨预测应用的方法应用。
Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time -series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra -trees Regressor were compared in the task of forecasting hourly rainfall volumes using time -series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the StackedLSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget -wise rainfall forecast applications.