Predicting non-deposition sediment transport in sewer pipes using Random forest

C Montes, Z Kapelan, J Saldarriaga - Water Research, 2021 - Elsevier
Sediment transport in sewers has been extensively studied in the past. This paper aims to
propose a new method for predicting the self-cleansing velocity required to avoid permanent …

Reliable prediction of the discharge coefficient of triangular labyrinth weir based on soft computing techniques

SM Seyedian, AH Haghiabi, A Parsaie - Flow Measurement and …, 2023 - Elsevier
Weirs are important hydraulic structures widely used to control the flow rates in open
channels and rivers. As a remarkable parameter, the discharge coefficient (Cd) determines …

Short to long-term forecasting of river flows by heuristic optimization algorithms hybridized with ANFIS

H Riahi-Madvar, M Dehghani, R Memarzadeh… - Water Resources …, 2021 - Springer
Accurate forecast of short-term to long-term streamflow prediction is of great importance for
water resources management. However, with the advent of novel hybrid machine learning …

[HTML][HTML] Enhancing flood prediction accuracy through integration of meteorological parameters in river flow observations: A case study Ottawa River

C Letessier, J Cardi, A Dussel, I Ebtehaj, H Bonakdari - Hydrology, 2023 - mdpi.com
Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is
crucial to incorporate temperature as an input variable in flood prediction models with …

Development of machine learning model for prediction of demolition waste generation rate of buildings in redevelopment areas

GW Cha, SH Choi, WH Hong, CW Park - International Journal of …, 2022 - mdpi.com
Owing to a rapid increase in waste, waste management has become essential, for which
waste generation (WG) information has been effectively utilized. Various studies have …

Hybridization of multivariate adaptive regression splines and random forest models with an empirical equation for sediment deposition prediction in open channel flow

MJS Safari - Journal of Hydrology, 2020 - Elsevier
It has been known that the channel cross-section shape impacts on flow velocity at sediment
deposition condition; however, existing models only apply to specific cross-section shapes …

Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

RM Adnan, ZM Yaseen, S Heddam, S Shahid… - International Journal of …, 2022 - Elsevier
Reliable modeling of river sediments transport is important as it is a defining factor of the
economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to …

[HTML][HTML] Machine learning approach for water quality predictions based on multispectral satellite imageries

V Anand, B Oinam, S Wieprecht - Ecological Informatics, 2024 - Elsevier
Water quality analysis is a vital component of the water resources management and has to
be undertaken promptly to make sure environmental regulations are being followed and to …

Derivation of optimized equations for estimation of dispersion coefficient in natural streams using hybridized ANN with PSO and CSO algorithms

HR Madvar, M Dehghani, R Memarzadeh… - IEEE …, 2020 - ieeexplore.ieee.org
In this paper, a new hybrid model is developed to improve the accuracy in the prediction of
the longitudinal dispersion coefficient () and the derivation of novel optimized explicit …

Terrestrial water storage anomaly estimating using machine learning techniques and satellite‐based data (a case study of Lake Urmia Basin)

K Soltani, A Azari - Irrigation and Drainage, 2024 - Wiley Online Library
In this study, the Terrestrial Water Storage Anomaly (TWSA) in the Lake Urmia Basin (LUB)
was obtained by using the GRACE satellites. The whole study area was covered by 10 …