A comparative study of extensive machine learning models for predicting long‐term monthly rainfall with an ensemble of climatic and meteorological predictors

Zhou, Zhengzheng; Ren, Jie; He, Xiao-Gang

doi:10.1002/hyp.14424

Cited by 27 publications

(9 citation statements)

References 95 publications

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“…The RMSE and MAE range from 48 mm to 79 mm and from 35 mm to 51 mm, respectively. It presents analogous ranges of the evaluation metrics with the previous predictions at the lower reach of the Yangtze River [25], illustrating the models in this study perform in the reasonable range. Among the base models, ANN at Xujiahui had the best prediction accuracy with the highest R 2 and the smallest RMSE and MAE, while the accuracy of KNN was the worst in terms of the three metrics at almost all the stations.…”

Section: Intercomparison Of Model Performancessupporting

confidence: 73%

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

Zhou

Chalov

et al. 2022

Water

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The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions.

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Section: Intercomparison Of Model Performancessupporting

confidence: 73%

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

Zhou

Chalov

et al. 2022

Water

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“…In particular, precipitation forecasts up to several months in advance can provide vital information for early warning of disasters and socioeconomic decision‐making for regional water management (Hao et al, 2018; Xu et al, 2020). However, prediction at the monthly time scale is challenging due to complicated mechanisms subjected to fast damping of atmospheric initial signals and inadequate representation of boundary conditions (Pegion et al, 2019; Wu et al, 2022; Zhou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Monthly precipitation prediction at regional scale using deep convolutional neural networks

Ni,

Wang,

Singh

et al. 2023

Hydrological Processes

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Variations in monthly precipitation are associated with climate extremes having significant socio‐economic and eco‐environmental impacts. Knowledge of monthly precipitation information is therefore valuable for policy making. Extensive research has been conducted on dynamic prediction using state‐of‐the‐art coupled climate models. However, the skilful prediction of monthly precipitation with dynamical models remains a challenge. With the development of machine learning tools, statistical predictions show comparable performance with dynamic models, but they are limited to at‐site monthly prediction, due to the lack of ability for processing spatially connected geophysical data. To improve monthly precipitation forecasting and provide regional forecasts, we propose a model termed UNet‐RegPre based on convolutional neural network and U‐net architecture. The model shows comparable prediction skills with recurrent state‐of‐the‐art dynamic forecasts (CFSv2), and has the capability to capture spatiotemporal patterns of precipitation and reproduce the main process of representative droughts. The key precursors for rainfall development identified by UNet‐RegPre shows that the constructed model can detect the climatic connection between rainfall in eastern China and summer monsoon, showing the potential to advance hydrometeorological understanding with a deep learning‐based model. These results suggest that the proposed model can be a potential tool for precipitation prediction.

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“…Rainfall prediction methods can be grouped under three categories: physical methods, statistical methods, and machine learning methods. The physical methods are conventional models that are developed using numerical weather prediction, rule-based approaches, or simulations and require a thorough description of the physical and dynamic processes of the interactions between the variables, i.e., the mathematical equations [2]. However, these models usually have a limited efficiency, computational capacity, and resolution [2].…”

Section: Introductionmentioning

confidence: 99%

“…The physical methods are conventional models that are developed using numerical weather prediction, rule-based approaches, or simulations and require a thorough description of the physical and dynamic processes of the interactions between the variables, i.e., the mathematical equations [2]. However, these models usually have a limited efficiency, computational capacity, and resolution [2]. The statistical methods aim to uncover the mathematical relationship and investigate the features of the historical time series, such as the autoregressive integrated moving average (ARIMA), the multivariate adaptive regression splines (MARS), and the Holt-Winters and hidden Markov models.…”

Section: Introductionmentioning

confidence: 99%

“…The dynamic nature of the atmosphere makes machine learning methods preferable over other approaches. The superiority of machine learning models over conventional models has been proven in many studies [2,3,5]. Due to the seriousness of the issue, building a machine learning-based model is highly desirable for the sustainable development of environmental systems.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars

Tuysuzoglu

Birant

2023

Sustainability

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Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction of rainfall is highly desirable and critical for environmentally sustainable development. In this study, an ensemble of K-stars (EK-stars) approach was proposed to predict the next-day rainfall status using meteorological data, such as the temperature, humidity, pressure, and sunshine, that were collected between the years 2007 and 2017 in Australia. This study also introduced the probability-based aggregating (pagging) approach when building and combining multiple classifiers for rainfall prediction. In the implementation of the EK-stars, different experimental setups were carried out, including the change of input parameter of the algorithm, the use of different methods in the pagging step, and whether the feature selection was performed or not. The EK-stars outperformed the original K-star algorithm and the recently proposed studies in terms of the classification accuracy by making predictions that were the closest to reality. This study shows that the proposed method is promising for generating accurate predictions for the sustainable development of environmental systems.

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A comparative study of extensive machine learning models for predicting long‐term monthly rainfall with an ensemble of climatic and meteorological predictors

Cited by 27 publications

References 95 publications

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

Monthly precipitation prediction at regional scale using deep convolutional neural networks

Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars

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