Gaussian mutation–orca predation algorithm–deep residual shrinkage network (DRSN)–temporal convolutional network (TCN)–random forest model: an advanced machine learning model for predicting monthly rainfall and filtering irrelevant data

• Published in: Environmental sciences Europe Vol. 36; no. 1; p. 13
• Main Authors: Ehteram, Mohammad, Afshari Nia, Mahdie, Panahi, Fatemeh, Shabanian, Hanieh
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Algorithms; Deep learning; Deep learning models; Earth and Environmental Science; Ecotoxicology; Environment; Feature selection; forests; Hybrid models; Hydrologic data; Learning algorithms; Machine learning; Mathematical models; meteorological data; Mutation; Normal distribution; Optimizers; Parameters; Particle swarm optimization; Pollution; Predation; prediction; Predictions; Predictive analytics; rain; Rainfall; Root-mean-square errors; shrinkage; system optimization; Temporal variations; Water monitoring; Water resources; Deep learning models; Feature selection; Hybrid models; Optimizers
• ISSN: 2190-4715; 2190-4715
• DOI: 10.1186/s12302-024-00841-9

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.