An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Singh, Abhinav Kumar; Kumar, Pankaj; Ali, Rawshan; Al‐Ansari, Nadhir; Vishwakarma, Dinesh Kumar; Kushwaha, Kuldeep Singh; Panda, Kanhu Charan; Sagar, Atish; Mirzania, Ehsan; Elbeltagi, Ahmed; Kuriqi, Alban; Heddam, Salim

doi:10.3390/su14138209

Cited by 60 publications

(12 citation statements)

References 151 publications

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“…A Gamma test establishes an impartial and multi-objective way of determining each input parameter's significant potential. Scholars use a tedious and timeconsuming trial-and-error method to determine the ideal input combination [30,31]. Therefore, to resolve this problem, a novel approach Gamma Test, is used to evaluate the ideal input variables in a data set, introduced by Stefansson et al [32].…”

Section: Best Input Variable Selection: Gamma Test (Gt)mentioning

confidence: 99%

“…We can produce a superior mathematical model if the gamma, standard error, and V-ratio are below zero; when the values of gamma, standard error, and V-ratio are lower, we have a higher chance of model consistency. Input pairings were selected from those that had the lowest gamma, standard error, and V-ratio values [1,14,31,34,40].…”

Section: Best Input Variable Selection: Gamma Test (Gt)mentioning

confidence: 99%

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Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

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In this study, four different soft computing AI techniques were tested for the prediction of sediment yield based on hydro-meteorological variables at Jondhara station, Seonath stream in Rajnandgaon district, India. In order to fulfill this purpose, the models namely, multilayer perceptron (MLP), support vector machine (SVM), multilayer perceptron coupled with genetic algorithm (MLP-GA), and support vector machine coupled with genetic algorithm (SVM-GA) models were employed. To select the optimal input variables, a statistical method such as the Gamma test was considered among several methods. Based on the results of the analysis, all models were evaluated by using the following statistical indices: Coefficient of Correlation (CC), room mean square error (RMSE) and percent bias (PBAIS). Overall, the performance of the studied models indicates that all of them are capable of simulation sediments yield at Jondhara station, Seonath river basin in a satisfactory manner. Comparison of results showed that the MLP-GA with CC = 0.988, RMSE = 0.006 and PBIAS = 0.000 in training period and CC= 0.990, RMSE = 0.007 and PBIAS = 0.000 in testing period for S-6 model and CC = 0.986, RMSE = 0.025 and PBIAS = -0.001 in training period and CC = 0.988, RMSE = 0.029 and PBIAS = -0.001 in testing period for S-13 model were able to yield better results than the other models considered. Furthermore, an SVM model is also observed to have some advantages over MLP models and SVM-GA models since it can represent the output data in a continuous manner by fitting a linear regression function to the output data, which has the advantage of making the model more precise than MLP and SVM-GA models.

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Section: Best Input Variable Selection: Gamma Test (Gt)mentioning

confidence: 99%

Section: Best Input Variable Selection: Gamma Test (Gt)mentioning

confidence: 99%

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

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“…R 2 help to determine whether the estimated and observed data are associated statistically (collinearity; Singh et al, 2022). In statistics, R 2 refers to the ratio of explained variation to total variation.…”

Section: Performance Criteriamentioning

confidence: 99%

“…In statistics, R 2 refers to the ratio of explained variation to total variation. Values F I G U R E 4 Flowchart of methodology of 0.5 or greater are generally considered acceptable; the higher the value, the less error variance (Singh et al, 2022). It is sensitive to outliers and insensitive to additive and proportional differences between observed and estimated data (Singh et al, 2022).…”

Section: Performance Criteriamentioning

confidence: 99%

Investigation of stage‐discharge model performance for streamflow estimating: A case study of the Gono River, Japan

Maghrebi

Vatanchi

Kawanisi

2023

River Research & Apps

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In water resource studies, long‐term measurements of river streamflow are essential. They allow us to observe trends and natural cycles and are prerequisites for hydraulic and hydrology models. This paper presents a new application of the stage‐discharge rating curve model introduced by Maghrebi et al. (2016) to estimate continuous streamflow along the Gono River, Japan. The proposed method, named single stage‐discharge (SSD) method, needs only one observed data to estimate the continuous streamflow. However, other similar methods require more than one observational data to fit the curve. The results of the discharge estimation by the SSD are compared with the improved fluvial acoustic tomography system (FATS), conventional rating curve (RC), and flow‐area rating curve (FARC). Some statistical indicators, such as the coefficient of determination (R2), root mean square error (RMSE), percent bias (PBAIS), mean absolute error (MAE), and Kling‐Gupta efficiency (KGE), are used to assess the performance of the proposed model. ADCP data are used as a benchmark for comparing four studied models. As a result of the comparison, the SSD method outperformed of FATS method. Also, the three studied RC methods were highly accurate at estimating streamflow if all observed data were used in calibration. However, if the observed data in calibration was reduced, the SSD method by R2 = 0.99, RMSE = 2.83 (m3/s), PBIAS = 0.715(%), MAE = 2.30 (m3/s), and KGE = 0.972 showed the best performance compared to other methods. It can be summarized that the SSD method is the feasible method in the data‐scarce region and delivers a strong potential for streamflow estimation.

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“…Schools of statistical inference focus on several different principles, the principle of repeated sampling, which depends on the arithmetic mean, and the principle of data collection, which depends on maintaining su cient data [7,8]. The classic iterative sampling methods are considered one of the most important methods used, but the Bayesian methods have become more common now as a result of the development of computers and the emergence of smart arithmetic methods [9].…”

Section: Introductionmentioning

confidence: 99%

Rayleigh-Monte Carlo Method for Image Noise Detection and Elimination

Abbas,

Ruhaima,

Hayder

et al. 2023

Preprint

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The issue of identifying areas of distortion in images is a common problem encountered in digital media processing. Traditional filtering methods often rely on setting thresholds that are dependent on the specific image being processed, leading to imprecise results. Adaptive filters have been proposed as a solution, but they are not without their technical difficulties. This paper presents a study on the effectiveness of the Rayleigh distribution method in identifying distortion areas in images. Monte Carlo estimation was used to estimate the distribution of image content, and the results showed that the Rayleigh distribution is highly efficient at identifying areas of distortion. The filtering process was then applied only to the regions with deformation, while ensuring that all other features of the image were preserved. The novelty of this study lies in the use of the Rayleigh distribution method to identify areas of distortion in images. The purpose of this study was to improve the quality of images by using a more precise filtering method that only targets distorted areas. The theoretical framework of this study is based on the application of the Rayleigh distribution method in digital media processing. The approach used in this study involved the use of Monte Carlo estimation to determine the effectiveness of the Rayleigh distribution method. The findings of this study show that the Rayleigh distribution method is highly effective in identifying areas of distortion in images, leading to higher quality images than traditional filtering methods. The research findings have practical implications for digital media processing industries, where the quality of images is paramount. By using the Rayleigh distribution method, images can be processed more efficiently, leading to improved quality and reduced processing time. Socially, improved image quality can have a positive impact on areas such as medical imaging, where accurate and high-quality images are crucial for diagnosis and treatment. Overall, this study offers an original and effective solution to the problem of identifying areas of distortion in images. By using the Rayleigh distribution method, images can be processed more precisely, leading to higher quality results. The use of Monte Carlo estimation provides a solid foundation for future research in this area.

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An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Cited by 60 publications

References 151 publications

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Investigation of stage‐discharge model performance for streamflow estimating: A case study of the Gono River, Japan

Rayleigh-Monte Carlo Method for Image Noise Detection and Elimination

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