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Your search keyword '"Ahmadianfar, Iman"' showing total 134 results
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134 results on '"Ahmadianfar, Iman"'

13. Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Author

Tao, Hai, Abba, Sani I., Al-Areeq, Ahmed M., Tangang, Fredolin, Samantaray, Sandeep, Sahoo, Abinash, Siqueira, Hugo Valadares, Maroufpoor, Saman, Demir, Vahdettin, Dhanraj Bokde, Neeraj, Goliatt, Leonardo, Jamei, Mehdi, Ahmadianfar, Iman, Bhagat, Suraj Kumar, Halder, Bijay, Guo, Tianli, Helman, Daniel S., Ali, Mumtaz, Sattar, Sabaa, Al-Khafaji, Zainab, Shahid, Shamsuddin, and Yaseen, Zaher Mundher

Published
2024
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34. Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism.

Author

Kordani, Marjan, Nikoo, Mohammad Reza, Fooladi, Mahmood, Ahmadianfar, Iman, Nazari, Rouzbeh, and Gandomi, Amir H.

Subjects
FLOOD forecasting, STANDARD deviations, FLOOD control, STATISTICAL errors, MACHINE learning
Abstract

Floods, as major natural disasters, cause massive property destruction and death. Understanding the occurrence time of this event by advance notice helps consider operational flood prevention systems and platforms. Precise flood forecasting provides a suitable time for policymakers and the public to consider helpful responses to this event. This study introduces an innovative methodology to enhance the precision of long-term flood predictions by employing a multistep forecasting approach. Our approach leverages historical time-series data on precipitation and streamflow to train an autoencoder algorithm. The primary objective is to develop advanced forecasting models to predict 12-step-weekly ahead flood occurrences during the critical April to July period from 2019 to 2021 within the DuPage River basin, Illinois, USA. In order to achieve this goal, we explore three deep learning techniques: bidirectional long short-term memory (BI-LSTM), ensemble long short-term memory (E-LSTM), and ensemble long short-term memory-gated recurrent unit (E-LSTM-GRU). Then, we integrate an attention mechanism (AM) that utilizes dynamic fusion techniques to emphasize the salient features of ensemble models. A dedicated fusion model is developed for each forecasting stage, effectively consolidating the predictions from various deep-learning models. Additionally, two traditional machine learning techniques, namely MLP and SVM models, are used to compare and justify the efficiency of applied deep learning models. The performance evaluation of our approach using statistical error metrics, including coefficient of determination (R2), normalized root mean square error, normalized mean absolute error, mean absolute percentage error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias, for the 12th step prediction reveals impressive results, with average values of 0.976, 2.393, 1.892, 20.956, 0.967, 0.923, and 2.307, respectively. These findings underscore the capability of our proposed models to significantly reduce uncertainty in flood forecasting, thus enhancing the reliability and accuracy of future predictions. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Discharge coefficient prediction of canal radial gate using neurocomputing models: an investigation of free and submerged flow scenarios.

Author

Tao, Hai, Jamei, Mehdi, Ahmadianfar, Iman, Khedher, Khaled Mohamed, Farooque, Aitazaz Ahsan, and Yaseen, Zaher Mundher

Subjects
DISCHARGE coefficient, MEAN square algorithms, RADIAL distribution function, MACHINE learning, STATISTICAL correlation, STANDARD deviations, ORIFICE plates (Fluid dynamics)
Abstract

In the current study, three machine learning (ML) models, i.e. Gaussian process regression (GPR), generalized regression neural network (GRNN), and multigene genetic programming (MGGP), were developed for predicting the discharge coefficient (Cd) of a radial gate under two different flow conditions, i.e. free and submerged. The modeling development of the flow and geometry input variables for the Cd was determined based on statistical correlations. We also performed a sensitivity analysis of the input variables for the Cd. The modeling results indicated that the developed ML models attained acceptable predictable performance; however, the prediction accuracy of the models was better under the free flow condition. In quantitative terms, the minimum root mean square error (RMSE) value was 0.010 using the GPR model and 0.019 using the MGGP model for the submerged and free flow conditions, respectively. The sensitivity analysis evidenced that the ratio of the gate opening height to the depth of water in the upstream (W:Yo) was the influential variable for the Cd under the free flow condition, whereas the ratio of the depth of water in the upstream to the depth of water in downstream (Yo:YT) was the influential variable for the Cd under the submerged flow condition. [ABSTRACT FROM AUTHOR]

Published
2022
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