A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting

Wang, Yinan; Yuan, Ze; Liu, Haoqi; Xing, Zhenxiang; Li, Heng; Fu, Qiang; Mo, Chongxun

doi:10.1016/j.eswa.2021.115872

Cited by 27 publications

(5 citation statements)

References 48 publications

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“…Projection of Water Supply and Demand-Related Indexes (1) Annual average precipitation The occurrence of annual precipitation events is random, which contributes to the difficulty in precipitation forecasting [77]. It is difficult to determine whether 2025 and 2030 will be wet years, normal years or dry years by some model forecasting methods, such as the ARIMA model.…”

Section: Classification Of the Evaluation Gradesmentioning

confidence: 99%

Multi-Level Fuzzy Comprehensive Evaluation for Water Resources Carrying Capacity in Xuzhou City, China

et al. 2023

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Water resources, as an essential natural resource, plays an irreplaceable role in the ecological environment, social economy, and human survival. Water resource carrying capacity (WRCC), as an important indicator of sustainable development, has been widely used to assess the capacity of water resources to support economic and social development. Using Xuzhou City as a case study, the sustainable capacity of water resources in the current (from 2012 to 2020) and future (projected scenarios in 2025 and 2030) stages were investigated by constructing a multi-level fuzzy-based evaluation model. The results indicated that the average WRCC score is 0.4388 in Xuzhou City, ranging from 0.2908 to 0.6330, with a significant decline in the score value of 0.4644 in 2019 but an apparent improvement in WRCC from 2012 to 2020. However, the continued pressure on water resources sustainable development is unchanged in Xuzhou, according to the projected assessment of WRCC in 2025 and 2030. Overall, the WRCC in Xuzhou City will be overloaded under future development scenarios, i.e., sustainable development mode (Scenario A), water conservation mode (Scenario B), rapid socioeconomic development mode (Scenario C), and adjustment of industrial structure mode (Scenario D). Thus, several measures, such as industrial restructuring and water conservation and utilization, should be conducted to enhance the carrying capacity of regional water resources and ensure the quality and sustainability of regional social and economic development. The results can provide a reference for the rational utilization of water resources in Xuzhou and are of some significance in promoting the city’s coordinated socioeconomic growth.

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Section: Classification Of the Evaluation Gradesmentioning

confidence: 99%

Multi-Level Fuzzy Comprehensive Evaluation for Water Resources Carrying Capacity in Xuzhou City, China

et al. 2023

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“…To improve the CEEMDAN-KRR and other comparative models by selecting the most relevant satellite variables, we optimise the overall predictive system using feature selections based on grey wolf optimisation (GWO), ant colony optimisation (ACO, [40]), atom search optimisation (ASO, [41]), and particle swarm optimisation (PSO) [42]. It is imperative to note that the CEEMDAN method is a variation of the Ensemble Empirical Mode Decomposition (EEMD) algorithm that provides a near-exact reconstruction of the original signal and a better spectral separation of the Intrinsic Mode Functions (IMFs) [43]. Several other comparison approaches include CEEMDAN-MLR or Multiple Linear Regression, CEEMDAN-RF or Random Forest, and CEEMDAN-SVR or Support Vector Regression, and their respective standalone counterparts such as KRR, MLR, RF, and SVR models are also used in this study.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors

et al. 2022

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Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture.

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“…In CEEMDAN-based decomposition, Gaussian white noise with a unit variance is added consecutively at each stage to reduce the forecasting procedure’s complexity (Di et al 2014 ). Over the last few years, CEEMDAN techniques have been successfully implemented in forecasting soil moisture (Ahmed et al 2021b ; Prasad et al 2018 , 2019a , b ), draught (Liu and Wang 2021 ), precipitation (Wang et al 2022 ), and wind energy (Liang et al 2020 ; Zhang et al 2017 ). However, a previous version (i.e., EEMD) was used in forecasting streamflow (Seo and Kim 2016 ) and rainfall (Beltrán-Castro et al 2013 ; Jiao et al 2016 ; Ouyang et al 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

Ahmed

Saha

et al. 2022

Stoch Environ Res Risk Assess

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The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop and compare the performances of different hybridised deep learning approaches with a convolutional neural network and long short-term memory referred to as CLSTM to forecast the daily UVI of Perth station, Western Australia. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is incorporated coupled with four feature selection algorithms (i.e., genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DEV)) to understand the diverse combinations of the predictor variables acquired from three distinct datasets (i.e., satellite data, ground-based SILO data, and synoptic mode climate indices). The CEEMDAN-CLSTM model coupled with GA appeared to be an accurate forecasting system in capturing the UVI. Compared to the counterpart benchmark models, the results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CEEMDAN-CLSTM model in apprehending the complex and non-linear relationships between predictor variables and the daily UVI. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

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A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting

Cited by 27 publications

References 48 publications

Multi-Level Fuzzy Comprehensive Evaluation for Water Resources Carrying Capacity in Xuzhou City, China

Multi-Level Fuzzy Comprehensive Evaluation for Water Resources Carrying Capacity in Xuzhou City, China

Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors

Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

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