[HTML][HTML] Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches

S Kumar, V Deshpande, M Agarwal… - Results in Engineering, 2024 - Elsevier
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 …

Performance evaluation of machine learning algorithms for the prediction of particle Froude number (Frn) using hyper-parameter optimizations techniques

D Shakya, V Deshpande, MJS Safari… - Expert Systems with …, 2024 - Elsevier
The sewer system is a critical component of urban infrastructure, responsible for transporting
wastewater and stormwater away from populated areas. Proper design and management of …

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 …, 2023 - Elsevier
The presence of vegetation in the water bodies has a profound effect on the flow velocity in
an open channel due to the resistance offered by it. In rivers, estuaries, and coastal …

Forecasting of time-dependent scour depth based on bagging and boosting machine learning approaches

S Kumar, G Oliveto, V Deshpande… - Journal of …, 2024 - iwaponline.com
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 …

A metaheuristic-based task offloading scheme with a trade-off between delay and resource utilization in IoT platform

N Kumari, PK Jana - Cluster Computing, 2024 - Springer
Fog computing has emerged as the most popular technology for processing delay-sensitive
tasks in the Internet of Things platform. However, offloading tasks to suitable fog nodes (FNs) …

Radial basis function regression (RBFR), ARRBFR models for estimation of particle Froude number in sewer pipes under deposited conditions

S Kumar, M Agarwal… - 2023 6th international …, 2023 - ieeexplore.ieee.org
The amount of water that can flow through a channel is affected by sediment deposition in
water drainage. Because of this, the self-cleaning mechanism is used a lot in sewer systems …

Estimation of particle Froude number in deposited bed condition using hybrid machine learning models

S Kumar, M Agarwal, V Deshpande - International conference on …, 2023 - Springer
In hydrology, maintaining self-cleaning capabilities in drainage systems is crucial to prevent
sediment deposition at the bottom of channels. This deposition can disrupt the hydraulic …

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… - Journal of …, 2024 - iwaponline.com
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 …

Predict Total Sediment Load Using Standalone and Ensemble Machine Learning Models

S Kumar, M Agarwal, V Deshpande - International Conference on …, 2023 - Springer
Sediment load includes bed and suspended loads. Bed load is sediment on a river's bottom,
while a suspended load is sediment floating in water currents. The nonlinear and …

Performance Evaluation of Thresholding-Based Segmentation Algorithms for Aerial Imagery

AD Bhattacharjee, S Dey, S Sarkar - International Conference on …, 2023 - Springer
The effectiveness of various threshold-based segmentation algorithms is examined in this
study utilizing aerial images, with a focus on accuracy metrics including intersection over …