Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting

Barrera-Animas, Ari Yair; Oyedele, Lukumon O.; Bilal, Muhammad; Akinosho, Taofeek Dolapo; Delgado, Juan Manuel Dávila; Akanbi, Lukman

doi:10.1016/j.mlwa.2021.100204

Cited by 127 publications

(79 citation statements)

References 24 publications

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“…More importantly, optimization algorithms such as stochastic gradient descent (SGD), root mean square propagation (RMSprop), adaptive grad (AdaGrad), adaptive delta (AdaDelta), adaptive moment estimation (Adam), adaptive maximum (Adamax), and Nesterov‐accelerated adaptive moment estimation (Nadam) are absent in the typhoon rainfall forecasting model based on the DL model (Huang et al., 2018; Lin & Chen, 2005; Lin & Wu, 2009; Wei & Chou, 2020). These optimization algorithms have successfully been applied in rainfall forecasting (Barrera‐Animas et al., 2021; Fadilah et al., 2021; Manoj & Ananth, 2020; Prasetya & Djamal, 2019; Sari et al., 2020; Zhang et al., 2018), spatial prediction of landslides (Bui et al., 2019), wind speed and wind direction forecasting (Puspita Sari et al., 2020; Saputri et al., 2020), evapotranspiration forecasting (Walls et al., 2020), run‐off forecasting (Nath et al., 2021), air quality index prediction (H. He & Luo, 2020), river stage, flash flood susceptibility and streamflow forecasting (Hitokoto et al., 2017; Rahimzad et al., 2021; Tien Bui et al., 2020), water demand forecasting (Koo et al., 2021), temperature and global solar radiation prediction (Del & Starchenko, 2021; Ghimire et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Effects of Learning Rates and Optimization Algorithms on Forecasting Accuracy of Hourly Typhoon Rainfall: Experiments With Convolutional Neural Network

Uddin

Sattar

et al. 2022

Earth and Space Science

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Section: Introductionmentioning

confidence: 99%

Effects of Learning Rates and Optimization Algorithms on Forecasting Accuracy of Hourly Typhoon Rainfall: Experiments With Convolutional Neural Network

Uddin

Sattar

et al. 2022

Earth and Space Science

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“…Forecasting heavy precipitation is an important function in estimating the runoff and flooding in the short to medium term [1][2][3][4], flood warning [5], real-time flood forecasting [6], and flood mitigation [7,8]. Nonetheless, rainfall directly affects runoff generation in streams, rivers, and even floods, making it one of the most specific hydrological phenomena [2].…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, rainfall directly affects runoff generation in streams, rivers, and even floods, making it one of the most specific hydrological phenomena [2]. The socioeconomic impacts of rainfall are significant, from physical damage in floods to disruptions in transport networks [3,9]. Simultaneously, India is challenged with increasing population and climate change, which have threatened the present freshwater need for irrigation and drinking [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

et al. 2022

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Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.

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“…Precipitation forecasting is closely related to human society. Accurate precipitation forecasts, especially rainstorms, are of great significance to protect life and property (Barrera-Animas et al, 2022). Numerical weather prediction (NWP) predicts future atmospheric states from observed weather by solving atmospheric equations numerically, and is perceived as the most commonly used method for precipitation forecasting (Bauer et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A hybrid fusion precipitation bias correction approach for Yin‐He global spectral model

Yin

Zhang

et al. 2022

Meteorological Applications

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In this article, we explored the performance of several fusion strategies for bias correction of precipitation on U‐Net‐based‐models and proposed a simple but efficient hybrid fusion strategy. The geopotential, vertical velocity, specific humidity and 3‐h cumulative precipitation from Yin‐He global spectral model (YHGSM) re‐forecast products are used as multiple correction factors, and the 3‐h cumulative precipitation calculated from ERA5 are used as labels. Experimental results reveal that, our hybrid fusion strategy can reduce the number of parameters by about 40% and obtain better fraction skill score (FSS) and threat score (TS). For TS of 0.1, 3.0, 10.0 and 20. 0 mm, U‐Net with hybrid fusion improves 31.8%, 97.0%, 314.0% and 576.0% than YHGSM, improves 1.3%, 3.0%, 3.6% and 10.5% than the classical U‐Net. This hybrid fusion strategy should provide a feasible approach to utilize multiple information inputs more efficiently in geophysics field.

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Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting

Cited by 127 publications

References 24 publications

Effects of Learning Rates and Optimization Algorithms on Forecasting Accuracy of Hourly Typhoon Rainfall: Experiments With Convolutional Neural Network

Effects of Learning Rates and Optimization Algorithms on Forecasting Accuracy of Hourly Typhoon Rainfall: Experiments With Convolutional Neural Network

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

A hybrid fusion precipitation bias correction approach for Yin‐He global spectral model

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