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» Authors » Aitazaz Ahsan Farooque

Aitazaz Ahsan Farooque

Explore the profile of Aitazaz Ahsan Farooque including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 5
Citations 11
Followers 0
Related Specialties
Science
Environmental Health
Top 10 Co-Authors
Mumtaz Ali
Zaher Mundher Yaseen
Mehdi Jamei
Masoud Karbasi
Changhyun Jun
Sayed M Bateni
Hafiz Mohkum Hammad
Artemi Cerda
Snowie Jane C Galgo
Wajid Ishaq
Published In
Sci Rep
J Environ Manage
Biochar
Environ Pollut
Environ Sci Pollut Res Int
Affiliations
Soon will be listed here.
Recent Articles
1.
A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting
Ahmadianfar I, Farooque A, Ali M, Jamei M, Jamei M, Yaseen Z
Sci Rep . 2025 Mar; 15(1):7596. PMID: 40038331
The precise monitoring and timely alerting of river water levels represent critical measures aimed at safeguarding the well-being and assets of residents in river basins. Achieving this objective necessitates the...
2.
Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning algorithms
Khosravi K, Farooque A, Karbasi M, Ali M, Heddam S, Faghfouri A, et al.
Environ Sci Pollut Res Int . 2025 Feb; PMID: 39994118
Water quality modeling in riverine systems is crucial for effective water resource management and pollution mitigation planning. However, the intricate interplay of anthropogenic activities with hydrological, climatic, and fluvial processes...
3.
Advancing modified biochar for sustainable agriculture: a comprehensive review on characterization, analysis, and soil performance
Fakhar A, Galgo S, Canatoy R, Rafique M, Sarfraz R, Farooque A, et al.
Biochar . 2025 Jan; 7(1):8. PMID: 39758611
Graphical Abstract:
4.
Publisher Correction: Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
Karbasi M, Ali M, Bateni S, Jun C, Jamei M, Farooque A, et al.
Sci Rep . 2024 Aug; 14(1):18517. PMID: 39122915
No abstract available.
5.
Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
Karbasi M, Ali M, Bateni S, Jun C, Jamei M, Farooque A, et al.
Sci Rep . 2024 Jul; 14(1):15051. PMID: 38951605
Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers...
6.
Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction
Hoy Z, Phuang Z, Farooque A, Fan Y, Woon K
Environ Pollut . 2024 Jan; 344:123386. PMID: 38242306
Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future....
7.
A review of soil carbon dynamics resulting from agricultural practices
Abbas F, Hammad H, Ishaq W, Farooque A, Bakhat H, Zia Z, et al.
J Environ Manage . 2020 Jun; 268:110319. PMID: 32510455
Literature related to the carbon cycle and climate contains contradictory results with regard to whether agricultural practices increase or mitigate emission of greenhouse gases (GHGs). One opinion is that anthropogenic...