Multi-model streamflow prediction using conditional bias-penalized multiple linear regression

Jozaghi, Ali; Shen, Haojing; Ghazvinian, Mohammadvaghef; Seo, Dong‐Jun; Welles, E.; Reed, Seann

doi:10.1007/s00477-021-02048-3

Cited by 17 publications

(7 citation statements)

References 60 publications

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“…The simple arithmetic averaging distributes equal weights among hydrological models, mirroring the theoretical approach of EWA. EWA: Equal-weighted average of individual forecast [39] Mean: Merging without assigning any specific weight [39] Trimmed Mean: Trim outlier before averaging [4] Weighted Granger and Newbold Methods: Weight estimation with (type 1) or without (type 2) correlations consideration [41] WAM-OLS: Weight estimation through multiple linear regression [42] RTMOCM: Linear Transfer Function Model integration [43] RSWM: Runoff-scale weighting with flow stages [44] WAM-MOGWO: Multi-objective grey wolf optimization for weighting [41] Granger and Ramanathan Methods GRA: Ordinary least squares (OLS)-based weighting Easy and quick implementation; Hedging against the use of bad model; Only GRC has bias correction steps GRA cannot construct density forecasts; GRC sometimes produces unrealistic results [41] GRB: Weights are constrained unity [41] GRC: Weighting with a bias correction [7] Regression Methods PLSR: Regression coefficient estimation using partial OLS [31] CCR: Bias correction with a constant term [39] PCR: Transfer predictor variable to orthogonal variables [45] SWR: Lagged forecast error and unbiased forecast [46] NGR: Gaussian mean as regression coefficient [47] CBP-MLR: Conditional bias incorporation for error reduction [47] Com-MLR: Weighted average of MLR and CBP-MLR [2] Bayesian QR-BMA: Quantile regression-based BMA [48] BMA-LR: BMA in linear regression model [48] BMA-GLM: BMA in the generalized linear model [49] SBMC: Sequential addition of new information [50] BMA-WVC: BMA-based ensemble multi-wavelet Volterra coupled approach [51] CBMA: Copula function integrated BMA [52] HBMA: Hierarchical BMA for uncertainty segregation [53] CBP-BMA: Copula Bayesian Processor with BMA for PDF relaxation [54] (e-Bay) BMA: Ensemble-based dynamic Bayesian averaging [55] BMA-DSRC: BMA integrated with the dynamic system response curve [56] VB-LSTM: Regression-based Variational Bayesian Long Short-Term Memory…”

Section: Simple Averaging Methods (Sam)mentioning

confidence: 99%

“…Regression-based techniques have gained popularity in multi-model forecast merging due to their ease of implementation and higher performance (mean NSE value around 0.83) compared to simple or weighted averaging methods (Figure 2). One commonly used method is the MLR method, which has been widely applied in various studies [12,26,29,31,47,84]. Two variations of MLR methods are Constrained MLR and Unconstrained MLR.…”

Section: Regression-based Methodsmentioning

confidence: 99%

“…While MLR is attractive due to its simplicity and interpretability, it can have detrimental effects on extreme value estimation due to conditional bias [65]. Conditional bias-penalized MLR (CBP-MLR) and Composite MLR (Com-MLR) are techniques that can mitigate these issues [47]. CPB-MLR can reduce a linearly weighted sum of squared errors by incorporating conditional bias, while Com-MLR is defined as the linearly weighted average of MLR and CBP-MLR.…”

Section: Regression-based Methodsmentioning

confidence: 99%

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Review of Recent Developments in Hydrologic Forecast Merging Techniques

Sheikh,

Coulibaly

2024

Water

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Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments.

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Section: Simple Averaging Methods (Sam)mentioning

confidence: 99%

Section: Regression-based Methodsmentioning

confidence: 99%

Section: Regression-based Methodsmentioning

confidence: 99%

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Review of Recent Developments in Hydrologic Forecast Merging Techniques

Sheikh,

Coulibaly

2024

Water

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“…Penalized Linear Regression estimates significant interactions between features in an n-by-p data matrix and the continuous outcome [24]. It can be used for efficiently handling the data when the number of features including molecular descriptors, exceeds the number of compound samples [25].…”

Section: Regression Penalized Linear Regressionmentioning

confidence: 99%

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Lee

Maria-Solano

et al. 2022

Biochemical Society Transactions

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There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

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“…MRL analysis is a method that can measure the level of influence of the relationship between predictors and outcomes. MRL is an analytical method that can improve the prediction process [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drug Demand Based on Deep Learning Approach and Classification Model

Yanto¹,

Arlis²,

Putra³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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The high demand for drugs in the last period has caused problems with drug shortages in several pharmacies. Almost all pharmacies experience the same problem, causing many people who do not to get their drug needs during the current pandemic. To overcome this, analyzing the process of predicting drug demand in the next period is necessary. The prediction process can be used as an initial solution in solving problems to see the number of drug demand numbers that will occur. This study aims to develop a predictive analysis model for drug demand using a deep learning approach and a classification model. Deep learning is an approach that does well in the case of prediction. The classification model also includes the right concept for solving the problem. The prediction and classification analysis methods include K-Means clustering, Multiple Linear Regression (MRL), Artificial Neural Network (ANN), and Decision Tree algorithms C.45. This method can provide better performance results in the prediction process to get precise and accurate output. Prediction results obtained from the learning process provide an accuracy rate of 99.99%. The output of the classification model also provides an overview of the knowledge base in the form of a decision tree. The level of classification model testing carried out gives the accuracy of the classification pattern of 97.05% so that the analytical model developed can predict future drug demand.

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Multi-model streamflow prediction using conditional bias-penalized multiple linear regression

Cited by 17 publications

References 60 publications

Review of Recent Developments in Hydrologic Forecast Merging Techniques

Review of Recent Developments in Hydrologic Forecast Merging Techniques

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Prediction of Drug Demand Based on Deep Learning Approach and Classification Model

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