Abstract

Monitoring water resources requires accurate predictions of rainfall data. Our study introduces a novel deep learning model named the deep residual shrinkage network (DRSN)—temporal convolutional network (TCN) to remove redundant features and extract temporal features from rainfall data. The TCN model extracts temporal features, and the DRSN enhances the quality of the extracted features. Then, the DRSN–TCN is coupled with a random forest (RF) model to model rainfall data. Since the RF model may be unable to classify and predict complex patterns and data, our study develops the RF model to model outputs with high accuracy. Since the DRSN–TCN model uses advanced operators to extract temporal features and remove irrelevant features, it can improve the performance of the RF model for predicting rainfall. We use a new optimizer named the Gaussian mutation (GM)–orca predation algorithm (OPA) to set the DRSN–TCN–RF (DTR) parameters and determine the best input scenario. This paper introduces a new machine learning model for rainfall prediction, improves the accuracy of the original TCN, and develops a new optimization method for input selection. The models used the lagged rainfall data to predict monthly data. GM–OPA improved the accuracy of the orca predation algorithm (OPA) for feature selection. The GM–OPA reduced the root mean square error (RMSE) values of OPA and particle swarm optimization (PSO) by 1.4%–3.4% and 6.14–9.54%, respectively. The GM–OPA can simplify the modeling process because it can determine the most important input parameters. Moreover, the GM–OPA can automatically determine the optimal input scenario. The DTR reduced the testing mean absolute error values of the TCN–RAF, DRSN–TCN, TCN, and RAF models by 5.3%, 21%, 40%, and 46%, respectively. Our study indicates that the proposed model is a reliable model for rainfall prediction.

中文翻译：

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高斯变异-逆戟鲸捕食算法-深度残差收缩网络（DRSN）-时间卷积网络（TCN）-随机森林模型：用于预测每月降雨量和过滤不相关数据的先进机器学习模型

摘要

监测水资源需要准确预测降雨数据。我们的研究引入了一种新颖的深度学习模型，称为深度残差收缩网络（DRSN）——时间卷积网络（TCN），以去除冗余特征并从降雨数据中提取时间特征。TCN模型提取时间特征，DRSN增强提取特征的质量。然后，DRSN-TCN 与随机森林 (RF) 模型相结合来对降雨数据进行建模。由于 RF 模型可能无法对复杂的模式和数据进行分类和预测，因此我们的研究开发了 RF 模型来对输出进行高精度建模。由于DRSN-TCN模型使用先进的算子来提取时间特征并去除不相关的特征，因此可以提高RF模型预测降雨的性能。我们使用名为高斯突变（GM）-逆戟鲸捕食算法（OPA）的新优化器来设置 DRSN-TCN-RF（DTR）参数并确定最佳输入场景。本文引入了一种新的降雨预测机器学习模型，提高了原始TCN的准确性，并开发了一种新的输入选择优化方法。该模型使用滞后的降雨数据来预测每月数据。GM-OPA 提高了逆戟鲸捕食算法 (OPA) 特征选择的准确性。GM-OPA 使 OPA 和粒子群优化 (PSO) 的均方根误差 (RMSE) 值分别降低了 1.4%–3.4% 和 6.14–9.54%。GM-OPA 可以简化建模过程，因为它可以确定最重要的输入参数。此外，GM-OPA可以自动确定最佳输入场景。DTR 将 TCN-RAF、DRSN-TCN、TCN 和 RAF 模型的测试平均绝对误差值分别降低了 5.3%、21%、40% 和 46%。我们的研究表明，所提出的模型是降雨预测的可靠模型。