Applications of machine learning to water resources management: A review of present status and future opportunities

AA Ahmed, S Sayed, A Abdoulhalik, S Moutari… - Journal of Cleaner …, 2024 - Elsevier
Water is the most valuable natural resource on earth that plays a critical role in the socio-
economic development of humans worldwide. Water is used for various purposes, including …

[HTML][HTML] Models and methods for quantifying the environmental, economic, and social benefits and challenges of green infrastructure: A critical review

Y Jezzini, G Assaf, RH Assaad - Sustainability, 2023 - mdpi.com
With growing urbanization and increasing climate change-related concerns, green
infrastructures (GIs) are recognized as promising solutions for mitigating various challenges …

[HTML][HTML] Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network

MA Al Mehedi, A Amur, J Metcalf, M McGauley… - Journal of …, 2023 - Elsevier
The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified
through numerical models based on hydrologic parameters and physics-based equations …

Performance, microbial community evolution and neural network modeling of single-stage nitrogen removal by partial-nitritation/anammox process

P Antwi, D Zhang, W Luo, L wen Xiao, J Meng… - Bioresource …, 2019 - Elsevier
Single-stage nitrogen removal by anammox/partial-nitritation (SNAP) process was proposed
and explored in a packed-bed-EGSB reactor to treat nitrogen-rich wastewater. With …

Fungal biosynthesis of lignin-modifying enzymes from pulp wash and Luffa cylindrica for azo dye RB5 biodecolorization using modeling by response surface …

CD Fernandes, VRS Nascimento, DB Meneses… - Journal of hazardous …, 2020 - Elsevier
This study demonstrates the evaluation between the artificial neural network technique
coupled to the genetic algorithm (ANN-GA) and the response surface methodology (RSM) …

Removal of methylene blue via bioinspired catecholamine/starch superadsorbent and the efficiency prediction by response surface methodology and artificial neural …

N Mahmoodi-Babolan, A Heydari… - Bioresource …, 2019 - Elsevier
This paper demonstrates coupling of the artificial neural network (ANN) technique with the
particle swarm optimization (PSO) method and compares the performance of ANN-PSO with …

[HTML][HTML] Evaluating different machine learning methods to simulate runoff from extensive green roofs

EMH Abdalla, V Pons, V Stovin… - Hydrology and Earth …, 2021 - hess.copernicus.org
Green roofs are increasingly popular measures to permanently reduce or delay storm-water
runoff. The main objective of the study was to examine the potential of using machine …

New input selection procedure for machine learning methods in estimating daily global solar radiation

SM Biazar, V Rahmani, M Isazadeh, O Kisi… - Arabian Journal of …, 2020 - Springer
Selection of optimal model inputs is a challenging issue particularly for non-linear and
dynamic systems. In this study, a new input selection method, procrustes analysis (PA), was …

[HTML][HTML] Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

Y Yang, TFM Chui - Hydrology and Earth System Sciences, 2021 - hess.copernicus.org
Sustainable urban drainage systems (SuDS) are decentralized stormwater management
practices that mimic natural drainage processes. The hydrological processes of SuDS are …

Stormwater runoff reduction benefits of distributed curbside infiltration devices in an urban catchment

H Shahzad, B Myers, J Boland, G Hewa, T Johnson - Water Research, 2022 - Elsevier
Distributed infiltration systems can benefit downstream water bodies by reducing the runoff
flowrate and volume discharges from the catchment. Investigating their runoff flowrate and …