Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach

Prasad, Ramendra; Deo, Ravinesh C.; Li, Yan; Maraseni, Tek

doi:10.1016/j.catena.2019.02.012

Cited by 110 publications

(51 citation statements)

References 89 publications

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“…The classification performed in this method is fulfilled by voting of multiple unbiased weak classifiers-decision trees [60]. The method has been successfully used and is strongly recommended [61].…”

Section: Feature Selection Of Input Variables (Boruta)mentioning

confidence: 99%

“…Finally, the Boruta algorithm ends when all attributes are confirmed or rejected or it reaches a specified limit of random forest runs. Noted that unassigned inputs after reaching limitation are classed as "Tentative" and obtain final decision (confirmed or rejected) by comparing the respective median Z scores with the median Z-scores of the best shadow variable [61].…”

Section: Feature Selection Of Input Variables (Boruta)mentioning

confidence: 99%

See 1 more Smart Citation

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

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Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

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Section: Feature Selection Of Input Variables (Boruta)mentioning

confidence: 99%

Section: Feature Selection Of Input Variables (Boruta)mentioning

confidence: 99%

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

View full text Add to dashboard Cite

show abstract

“…The MTS is a series of high-dimensional vectors, such as hydrological data [25], electroencephalogram (EEG) data [26], and flight data [27].…”

Section: Model Constructionmentioning

confidence: 99%

A multivariate grey incidence model for different scale data based on spatial pyramid pooling

Zhang

Yin

2020

J. of Syst. Eng. Electron.

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In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling. Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly, Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.

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“…Piecewise cubic or linear splines to identify the local fit are used by MARS and it develops an adaptive procedure to architect the optimal model. It has been proved that the MARS model could be successfully applied for different cross-disciplinary fields, especially in hydrological applications [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan

Liang

El-Shafie

et al. 2019

Water

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Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China-Guangyuan and Beibei-were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE). The data period covers 01/04/2007-12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

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Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach

Cited by 110 publications

References 89 publications

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

A multivariate grey incidence model for different scale data based on spatial pyramid pooling

Prediction of Suspended Sediment Load Using Data-Driven Models

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