Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Chitsaz, Nastaran; Azarnivand, Ali; Araghinejad, Shahab

doi:10.1080/02626667.2015.1085991

Cited by 26 publications

(13 citation statements)

References 61 publications

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“…These studies were able to provide the estimated climate signals. Bretherton et al [31] compared four different techniques (PCA, CCA, SVD, and single-field PCA) and Chitsaz et al [52] compared SVD with PCA, and both concluded that SVD was the easiest method to implement and superior to the other methods. It is easy to implement as it untangles data into independent components that are easy to analyze.…”

Section: Statistical Approachmentioning

confidence: 99%

Land–Ocean–Atmosphere Influences on Groundwater Variability in the South Atlantic–Gulf Region

2020

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Climate association between Groundwater Storage (GWS) and sea level changes have been missing from the Intergovernmental Panel on Climate Change, demanding a requisite study of their linkage and responses. Variability in the Hydrologic Unit Code—03 region, i.e., one of the major U.S. watersheds in the southeast caused by Sea Surface Temperature (SST) variability in the Pacific and Atlantic Ocean, was identified. Furthermore, the SST regions were identified to assess its relationship with GWS, sea level, precipitation, and terrestrial water storage. Temporal and spatial variability were obtained utilizing the singular value decomposition statistical method. A gridded GWS anomaly from the Gravity Recovery and Climate Experiment (GRACE) was used to understand the relationship with sea level and SST. The negative pockets of SST were negatively linked with GWS. The identification of teleconnections with groundwater may substantiate temporal patterns of groundwater variability. The results confirmed that the SST regions exhibited El Niño Southern Oscillation patterns, resulting in GWS changes. Moreover, a positive correlation between GWS and sea level was observed on the east coast in contrast to the southwestern United States. The findings highlight the importance of climate-driven changes in groundwater attributing changes in sea level. Therefore, SST could be a good predictor, possibly utilized for prior assessment of variabilities plus groundwater forecasting.

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Section: Statistical Approachmentioning

confidence: 99%

Land–Ocean–Atmosphere Influences on Groundwater Variability in the South Atlantic–Gulf Region

2020

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“…Additionally, it is characterized by its low computational complexity ( 62 , 63 ). It is also worth mentioning that singular value decomposition demonstrated superior dimensionality reduction accuracy against principal component analysis according to a set of performance evaluation tests ( 64 , 65 ).…”

Section: Proposed Methodsmentioning

confidence: 99%

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abdelkader

Moselhi

Marzouk

et al. 2020

Transportation Research Record

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Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

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“…Thus, AI modeling has been widely used for streamflow forecasting in recent years because of the availability of long-term gauging data, the ever-increasing computational power. For example, Chitsaz et al [4] proposed a pre-processing based model to forecast monthly streamflow. They considered three data-driven models, generalized regression neural network (GRNN), multi-layer perceptron (MLP), and adaptive neuro fuzzy inference system (ANFIS) and pre-processing techniques, such as principal component analysis (PCA), singular value decomposition (SVD) and average values of inputs for their aims.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Rezaie-Balf

Nowbandegani

Phillips

et al. 2019

Water

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Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

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Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Cited by 26 publications

References 61 publications

Land–Ocean–Atmosphere Influences on Groundwater Variability in the South Atlantic–Gulf Region

Land–Ocean–Atmosphere Influences on Groundwater Variability in the South Atlantic–Gulf Region

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

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