AI-driven predictions of geophysical river flows with vegetation Sanjit Kumar, Mayank Agarwal, Vishal Deshpande, James R. Cooper, Khabat Khosravi, Namal Rathnayake, Yukinobu Hoshino, Komali Kantamaneni, Upaka Rathnayake Scientific Reports, 2024 In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R2 = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.
Forecasting of time-dependent scour depth based on bagging and boosting machine learning approaches Sanjit Kumar, Giuseppe Oliveto, Vishal Deshpande, Mayank Agarwal, Upaka Rathnayake Journal of Hydroinformatics, 2024 Forecasting the time-dependent scour depth (dst) is very important for the protection of bridge structures. Since scour is the result of a complicated interaction between structure, sediment, and flow velocity, empirical equations cannot guarantee an advanced accuracy, although they would preserve the merit of being straightforward and physically inspiring. In this article, we propose three ensemble machine learning methods to forecast the time-dependent scour depth at piers: extreme gradient boosting regressor (XGBR), random forest regressor (RFR), and extra trees regressor (ETR). These models predict the scour depth at a given time, dst, based on the following main variables: the median grain size, d50, the sediment gradation, σg, the approach flow velocity, U, the approach flow depth y, the pier diameter Dp, and the time t. A total of 555 data points from different studies have been taken for this research work. The results indicate that all the proposed models precisely estimate the time-dependent scour depth. However, the XGBR method performs better than the other methods with R = 0.97, NSE = 0.93, AI = 0.98, and CRMSE = 0.09 at the testing stage. Sensitivity analysis exhibits that the time-dependent scour depth is highly influenced by the time scale.
Efficient functioning of a sewer system: application of novel hybrid machine learning methods for the prediction of particle Froude number Sanjit Kumar, Bablu Kirar, Mayank Agarwal, Vishal Deshpande, Upaka Rathnayake Journal of Hydroinformatics, 2024 Sewer systems are usually built with a self-cleaning system that keeps the bottom of the channel free of sediment to lessen the effects of the constant buildup of sediment particles. Because of this, it is important to accurately predict the particle Froude number (Fr) when making sewer systems. For the prediction of Fr, five different sets of input variables were looked at. For the training and testing of the machine learning (ML) model, we used 10-fold cross-validation methodologies to prevent overfitting. M5Prime (M5P) model as a standalone and Bagging-M5P as a hybrid model were utilized, and the results were compared with the empirical equations proposed in the literature. Models perform best when all input variables are used for training and testing of models. The hybrid BA-M5P model performed better than the M5P model and empirical equations. We performed sensitivity analysis and compared the result based on MAE and MSE value, and we found sediment concentration (Svc) is the most important variable to predict the particle Froude number under non-deposition with deposited bed by best performing model BA-M5P. Hence, for the self-cleaning system, we prefer the BA-M5P ML model with Svc the most required variable.
Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches Sanjit Kumar, Vishal Deshpande, Mayank Agarwal, Upaka Rathnayake Results in Engineering, 2024 Sediment deposition has a substantial effect on the hydraulic capacity of channels in urban drainage and sewer systems. In this sense, the self-cleaning concept has been extensively used in the construction of urban drainage and sewer systems. In this regard, the design of the sewer system heavily depends on the accurate forecasting of the particle Froude number (Fr). This study is conducted on experimental data sets collected from existing studies, including a wide range of dimensionless grain size of particles (Dgr), sediment median size (d), hydraulic radius (R), volumetric sediment concentration (Cv), and pipe friction factor (λ) for the condition of clear bed. We forecasted the particle Froude number using four different input combinations. We employed the Random Tree (RT) and Reduced Error Pruning Tree (REPT) methods as standalone methods, as well as Random Committee (RC) and Bagging (BA) methods as hybrid machine learning (ML) methods in particle Froude number forecasting. Hybrid machine learning methods demonstrate enhanced performance compared to both standalone machine learning methods and empirical equations. In the context of sewer system design under non-deposited bed conditions, there is a need to accurately forecast the particle Froude number, RC-RT (IA = 0.96, R = 0.928, MSE = 0.718, RRMSE = 0.197, NSE = 0.86, and PBias = -0.284) performed the best, followed by BA-RT, RC-REPT, BA-REPT, RT, and REPT. In our research, it was found that, in forecasting the particle Froude number under non-deposited bed conditions, Cv emerges as the most responsive input parameter among others.
Standalone and Novel Hybrid ML Models for Estimating Stream Velocities in a River Channel with Vegetation S Kumar, M Agarwal, U Rathnayake Evolving Earth, 100115 , 2026 2026
Standalone and Hybrid machine learning approaches to predict sediment load in an alluvial channel S Kumar, V Deshpande, M Agarwal Engineering Applications of Artificial Intelligence 150, 110578 , 2025 2025 Citations: 2
Optimizing Intrusion Detection Systems Using Machine Learning for Enhanced Cybersecurity and Threat Prevention L Kumari, G Srivastava, S Kumar International Conference on Advancement In Cyber Security and Digital … , 2025 2025
The Power of AI in Banking 4.0: Enhancing Customer Experience and Operational Efficiency L Kumari, S Kumar, K Bhattacharjee, G Srivastava Proceedings of the 6th International Conference on Information Management … , 2024 2024
Anaphora Resolution through Salience in Magahi: A Syntactic and Semantic Analysis L Kumari, S Kumar, S Chandra, G Srivastava Proceedings of the 6th International Conference on Information Management … , 2024 2024
Efficient functioning of a sewer system: application of novel hybrid machine learning methods for the prediction of particle Froude number S Kumar, B Kirar, M Agarwal, V Deshpande, U Rathnayake Journal of Hydroinformatics 26 (8), 1929-1943 , 2024 2024
Forecasting of time-dependent scour depth based on bagging and boosting machine learning approaches S Kumar, G Oliveto, V Deshpande, M Agarwal, U Rathnayake Journal of Hydroinformatics 26 (8), 1906-1928 , 2024 2024 Citations: 11
AI-driven predictions of geophysical river flows with vegetation S Kumar, M Agarwal, V Deshpande, JR Cooper, K Khosravi, ... Scientific reports 14 (1), 16368 , 2024 2024 Citations: 23
Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches S Kumar, V Deshpande, M Agarwal, U Rathnayake Results in Engineering 22, 102320 , 2024 2024 Citations: 13
Predict Total Sediment Load Using Standalone and Ensemble Machine Learning Models S Kumar, M Agarwal, V Deshpande International Conference on Advances in Data-driven Computing and … , 2023 2023
Estimation of particle Froude number in deposited bed condition using hybrid machine learning models S Kumar, M Agarwal, V Deshpande International conference on advances in data-driven computing and … , 2023 2023 Citations: 4
Predicting flow velocity in a vegetative alluvial channel using standalone and hybrid machine learning techniques S Kumar, B Kumar, V Deshpande, M Agarwal Expert Systems with Applications , 2023 2023 Citations: 22
Radial Basis Function Regression (RBFR), AR-RBFR models for estimation of Particle Froude Number in sewer pipes under deposited conditions S Kumar, M Agarwal, V Deshpande 2023 6th International Conference on Information Systems and Computer … , 2023 2023 Citations: 8
Estimation of time dependent scour depth around circular bridge piers: Application of ensemble machine learning methods S Kumar, MK Goyal, V Deshpande, M Agarwal Ocean Engineering 270, 113611 , 2023 2023 Citations: 61
Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes S Kumar, B Kirar, M Agarwal, V Deshpande Natural Hazards 116 (3), 1823 - 1842 , 2022 2022 Citations: 13
Predicting the abrasion loss of open-graded friction course mixes with EAF steel slag aggregates using machine learning algorithms ML Pattanaik, S Kumar, R Choudhary, M Agarwal, B Kumar Construction and Building Materials 321, 126408 , 2022 2022 Citations: 12
Estimation of Time-Dependent Pier Scour Depth Using Ensemble and Boosting-Based Data-Driven Approaches S Kumar, M Agarwal, V Deshpande, MK Goyal International Conference on Hydraulics, Water Resources and Coastal … , 2021 2021 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Estimation of time dependent scour depth around circular bridge piers: Application of ensemble machine learning methods S Kumar, MK Goyal, V Deshpande, M Agarwal Ocean Engineering 270, 113611 , 2023 2023 Citations: 61
AI-driven predictions of geophysical river flows with vegetation S Kumar, M Agarwal, V Deshpande, JR Cooper, K Khosravi, ... Scientific reports 14 (1), 16368 , 2024 2024 Citations: 23
Predicting flow velocity in a vegetative alluvial channel using standalone and hybrid machine learning techniques S Kumar, B Kumar, V Deshpande, M Agarwal Expert Systems with Applications , 2023 2023 Citations: 22
Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches S Kumar, V Deshpande, M Agarwal, U Rathnayake Results in Engineering 22, 102320 , 2024 2024 Citations: 13
Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes S Kumar, B Kirar, M Agarwal, V Deshpande Natural Hazards 116 (3), 1823 - 1842 , 2022 2022 Citations: 13
Predicting the abrasion loss of open-graded friction course mixes with EAF steel slag aggregates using machine learning algorithms ML Pattanaik, S Kumar, R Choudhary, M Agarwal, B Kumar Construction and Building Materials 321, 126408 , 2022 2022 Citations: 12
Forecasting of time-dependent scour depth based on bagging and boosting machine learning approaches S Kumar, G Oliveto, V Deshpande, M Agarwal, U Rathnayake Journal of Hydroinformatics 26 (8), 1906-1928 , 2024 2024 Citations: 11
Radial Basis Function Regression (RBFR), AR-RBFR models for estimation of Particle Froude Number in sewer pipes under deposited conditions S Kumar, M Agarwal, V Deshpande 2023 6th International Conference on Information Systems and Computer … , 2023 2023 Citations: 8
Estimation of particle Froude number in deposited bed condition using hybrid machine learning models S Kumar, M Agarwal, V Deshpande International conference on advances in data-driven computing and … , 2023 2023 Citations: 4
Standalone and Hybrid machine learning approaches to predict sediment load in an alluvial channel S Kumar, V Deshpande, M Agarwal Engineering Applications of Artificial Intelligence 150, 110578 , 2025 2025 Citations: 2
Estimation of Time-Dependent Pier Scour Depth Using Ensemble and Boosting-Based Data-Driven Approaches S Kumar, M Agarwal, V Deshpande, MK Goyal International Conference on Hydraulics, Water Resources and Coastal … , 2021 2021 Citations: 1
Standalone and Novel Hybrid ML Models for Estimating Stream Velocities in a River Channel with Vegetation S Kumar, M Agarwal, U Rathnayake Evolving Earth, 100115 , 2026 2026
Optimizing Intrusion Detection Systems Using Machine Learning for Enhanced Cybersecurity and Threat Prevention L Kumari, G Srivastava, S Kumar International Conference on Advancement In Cyber Security and Digital … , 2025 2025
The Power of AI in Banking 4.0: Enhancing Customer Experience and Operational Efficiency L Kumari, S Kumar, K Bhattacharjee, G Srivastava Proceedings of the 6th International Conference on Information Management … , 2024 2024
Anaphora Resolution through Salience in Magahi: A Syntactic and Semantic Analysis L Kumari, S Kumar, S Chandra, G Srivastava Proceedings of the 6th International Conference on Information Management … , 2024 2024
Efficient functioning of a sewer system: application of novel hybrid machine learning methods for the prediction of particle Froude number S Kumar, B Kirar, M Agarwal, V Deshpande, U Rathnayake Journal of Hydroinformatics 26 (8), 1929-1943 , 2024 2024
Predict Total Sediment Load Using Standalone and Ensemble Machine Learning Models S Kumar, M Agarwal, V Deshpande International Conference on Advances in Data-driven Computing and … , 2023 2023
