A novel approach for the prediction of the incipient motion of sediments under smooth, transitional and rough flow conditions using Geno-Fuzzy Inference System model

Bizimana, Hussein; Altunkaynak, Abdüsselam

doi:10.1016/j.jhydrol.2019.123952

Cited by 12 publications

(8 citation statements)

References 71 publications

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“…Parameters represented in the first layer define the premise (antecedent) parameters. In Jang (1993) and Bizimana and Altunkaynak (2019), the fabrics and functionality of the ANFIS have been explained in details.…”

Section: Fig 3: Neuro-fuzzy Inference System Structurementioning

confidence: 99%

“…The Geno Fuzzy Inference System Model (GENOFIS) is a hybrid evolutionary technique proposed by Bizimana and Altunkaynak (2019). GENOFIS is an improvement of the conventional and widely accepted Sugeno Fuzzy Inference System, and is a robust compromise between computational complexity and high accuracy.…”

Section: Geno Fuzzy Inference System Modelmentioning

confidence: 99%

“…The model is trained on observed data from the gauge station located at the Kigali International Airport. Geno Fuzzy Inference System (GENOFIS) (Bizimana and Altunkaynak, 2019), Adaptive Neuro Fuzzy Inference System (ANFIS) (Jang, 1993), and Support Vector Machine (SVM) (Jakkula, 2006) were compared. While predictive models are always a challenge for future predictions, artificial intelligence and soft computing basedmethods are being adopted for the purposes of this study to evaluate the potential of this nondeterministic approach.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Rainfall and Climate Change Patterns in the City of Kigali via Machine Learning and IPCC Codex Models and their Impact on Heavy Storms

Bizimana,

Altunkaynak,

Kalin

et al. 2023

Preprint

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Rwanda has been negatively affected by a changing climate, exacerbated by human impact on land and water resources. In most parts of the country the rainfall pattern has changed over the last decades resulting in both enhanced flooding and water shortage / scarcity in much of the country, especially in the Capital City of Kigali and peripheries which is the main economic hub of the country with strong links to the East African region. Changes in precipitation have affected agricultural production, hydropower production, and water supplies, and has been a result of increased heavy storms, and flash floods in the city. This study developed a new predictive model rainfall patterns in the City of Kigali (CoK) in the Republic of Rwanda using evolutionary methodologies that apply machine learning techniques of Fuzzy Inference Systems (FIS) trained via Genetic Algorithms, Neuro Network Systems and a comparative Support Vector Machine tool, and assessment downscaled climate change combinations with predicted rainfall patterns. The models were calibrated and validated using measured rainfall data in the City of Kigali from 1991 through 2023. The model results show the developed Geno Fuzzy Inference System (GENOFIS) model performed better than the Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) models. The Coefficient of Efficiency (CE), and Root Mean Square Error (RMSE) were used as diagnostic measures for model performance evaluation. Models generated with GENOFIS are therefore recommended for rainfall and related prediction pattern in the City of Kigali for climate change adaptation and resilience policy and planning.

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Section: Fig 3: Neuro-fuzzy Inference System Structurementioning

confidence: 99%

Section: Geno Fuzzy Inference System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Rainfall and Climate Change Patterns in the City of Kigali via Machine Learning and IPCC Codex Models and their Impact on Heavy Storms

Bizimana,

Altunkaynak,

Kalin

et al. 2023

Preprint

Self Cite

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“…Next to numerical approximations, soft computing methods (e.g., artificial neural networks, fuzzy logic, and evolutionary computation) also tried to approximate real-life problems and so used to estimate the sediment transport in urban drainage systems ( [16][17][18][19][20][21]).…”

Section: Literature Reviewmentioning

confidence: 99%

Sediment Transport in Sewage Pressure Pipes, Part II: 1 D Numerical Simulation

Rinas

Fricke

Tränckner

et al. 2020

Water

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Urban drainage modelling is a state-of-the-art tool to understand urban water cycles. Nevertheless, there are gaps in knowledge of urban water modelling. In particular pressure drainage systems are hardly considered in the scientific investigation of urban drainage systems, although they represent an important link in its network structure. This work is the conclusion of a series of investigations that have dealt intensively with pressure drainage systems. In particular, this involves the transport of sediments in pressure pipes. In a real-world case study, sediment transport inside a pressure pipe in an urban region in northern Germany was monitored by online total suspended solids measurements. This in situ data is used in this study for the development and calibration of a sediment transport model. The model is applied to investigate sediments transport under low flow velocities (due to energy saving intentions). The resulting simulation over 30 days pumping operation shows that a transport of sediments even at very low flow velocities of 0.27 m/s and under various inflow conditions (dry weather and storm water inflow) is feasible. Hence, with the help of the presented sediment transport model, energy-efficient pump controls can be developed without increasing the risk of deposition formation.

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“…Extensive research on optimization methods in various areas of water engineering systems, such as reservoir operations, flood management [1][2][3][4], groundwater management [5][6][7], quality management of water resources [8][9][10][11], water distribution systems [12][13][14], and sedimentation [15][16][17], has attracted the attention of researchers to improve optimization algorithms to solve complex water engineering systems. Metaheuristic optimization methods (MOMs) have piqued the interest of many academics in recent years and have been extensively used for various real-world purposes.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems

Ahmadianfar¹,

Halder

Heddam

et al. 2023

Sustainability

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Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems’ performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN’s novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems.

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A novel approach for the prediction of the incipient motion of sediments under smooth, transitional and rough flow conditions using Geno-Fuzzy Inference System model

Cited by 12 publications

References 71 publications

Assessment of Rainfall and Climate Change Patterns in the City of Kigali via Machine Learning and IPCC Codex Models and their Impact on Heavy Storms

Assessment of Rainfall and Climate Change Patterns in the City of Kigali via Machine Learning and IPCC Codex Models and their Impact on Heavy Storms

Sediment Transport in Sewage Pressure Pipes, Part II: 1 D Numerical Simulation

An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems

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