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Refinado por: Lista de Todas as Versões Science Citation Index Expanded (Web of Science) remover
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1
Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework
Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework
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Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework

Achite, Mohammed ; Mohammadi, Babak ; Jehanzaib, Muhammad ; Elshaboury, Nehal ; Pham, Quoc Bao ; Duan, Zheng

Atmosphere, 2022-10, Vol.13 (10), p.1688 [Periódico revisado por pares]

Basel: MDPI AG

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2
Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis
Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis
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Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis

Ahmadi, Arman ; Olyaei, Mohammadali ; Heydari, Zahra ; Emami, Mohammad ; Zeynolabedin, Amin ; Ghomlaghi, Arash ; Daccache, Andre ; Fogg, Graham E. ; Sadegh, Mojtaba

Water (Basel), 2022-03, Vol.14 (6), p.949 [Periódico revisado por pares]

Basel: MDPI AG

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3
Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan
Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan
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Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan

Ahmed, Fahad ; Loc, Ho Huu ; Park, Edward ; Hassan, Muhammad ; Joyklad, Panuwat

Water (Basel), 2022-11, Vol.14 (21), p.3533 [Periódico revisado por pares]

Basel: MDPI AG

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4
Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia
Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia
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Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia

Al-Areeq, Ahmed ; Abba, S. ; Yassin, Mohamed ; Benaafi, Mohammed ; Ghaleb, Mustafa ; Aljundi, Isam

Remote sensing (Basel, Switzerland), 2022-11, Vol.14 (21), p.5515 [Periódico revisado por pares]

Basel: MDPI AG

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5
Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning
Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning
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Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Alsarayreh, Mohammad ; Mohamed, Omar ; Matar, Mustafa

Sustainability (Basel, Switzerland), 2022-01, Vol.14 (2), p.870 [Periódico revisado por pares]

Basel: MDPI AG

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6
Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia
Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia
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Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia

Ansari, Mozafar ; Othman, Faridah ; Abunama, Taher ; El-Shafie, Ahmed

Environmental science and pollution research international, 2018-04, Vol.25 (12), p.12139-12149 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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7
An error correction system for sea surface temperature prediction
An error correction system for sea surface temperature prediction
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An error correction system for sea surface temperature prediction

Araújo, Ricardo de A. ; de Mattos Neto, Paulo S. G. ; Nedjah, Nadia ; Soares, Sergio C. B.

Neural computing & applications, 2023-06, Vol.35 (16), p.11681-11699 [Periódico revisado por pares]

London: Springer London

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8
A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks
A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks
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A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks

Arriandiaga, A. ; Portillo, E. ; Sánchez, J. A. ; Cabanes, I. ; Pombo, I.

Neural computing & applications, 2016-08, Vol.27 (6), p.1577-1592 [Periódico revisado por pares]

London: Springer London

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9
Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks
Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks
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Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Asad, Muhammad ; Qamar, Usman ; Abbas, Muhammad Maietta, Saverio

Journal of healthcare engineering, 2021-02, Vol.2021, p.1-7 [Periódico revisado por pares]

Hindawi

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10
The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing
The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing
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The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing

Ashrafi, Mohammad ; Chua, Lloyd H. C. ; Quek, Chai

Hydrology Research, 2019-08, Vol.50 (4), p.991-1001 [Periódico revisado por pares]

London: IWA Publishing

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