Mehdi Fuladipanah
@iauramhormoz.ac.ir
Department of Civil Engineering, Ramhormoz Branch
Islamic Azad University, Ramhormoz, Iran
RESEARCH INTERESTS
Hydraulics, River engineering, sediment, hydrodynamics, artificial intelligent
Scopus Publications
Scopus Publications
Ozgur Kisi, Hazi Mohammad Azamathulla, Fatih Cevat, Christoph Kulls, Mehdi Kuhdaragh, and Mehdi Fuladipanah
Elsevier BV
Mehdi Fuladipanah, H. Md. Azamathulla, Ozgur Kisi, Mehdi Kouhdaragh, and Vishwandham Mandala
IWA Publishing
Abstract The intricate calculation of bed sediment load (BSL), which is influenced by hydraulic, hydrological, and sedimentary factors, is vital for informed decision-making in water resource management. Machine learning models, which are gaining popularity due to their accessibility and ability to reveal complex relationships, play a significant role in tackling these challenges. The efficacy of gene expression programming (GEP) models, support vector machines (SVMs), multi-layer perceptron (MLP), and multivariate adaptive regression splines (MARS) has been assessed through measured data of number 540 obtained from six rivers, namely Oak Creek, Nahal Yatir, Sagehen Creek, Elbow River, Jacoby River, and Goodwin Creek from 1954 to 1992. The assessment of model performance has been conducted utilizing root mean square error (RMSE), R2, Nash–Sutcliffe coefficient (NSE), and developed discrepancy ratio (DDR) as indices. Following data normalization within the range of 0–1, the data models underwent training and testing processes with a partition ratio of 80% for training and 20% for testing. Four dimensionless parameters, denoted as Fr = U/√gy, U/U*, Se, and ω = τU/γs√gyDs3, were employed as inputs in the models. The outcomes indicate that they exhibit superior performance compared to other methods, as evidenced by the following metrics in predicting BSL during the test stage: RMSE = 1.4088, NSE = 0.73054, R2 = 0.8729, and maximum QDDR(max) = 1.9564.
Mehdi Fuladipanah, H Md Azamathulla, Kiran Tota-Maharaj, Vishwanadham Mandala, and Aaron Chadee
Elsevier BV
Mehdi Fuladipanah, Mohammad Azamathulla Hazi, and Ozgur Kisi
Springer Science and Business Media LLC
AbstractThe study focuses on the critical concern of designing secure and resilient bridge piers, especially regarding scour phenomena. Traditional equations for estimating scour depth are limited, often leading to inaccuracies. To address these shortcomings, modern data-driven models (DDMs) have emerged. This research conducts a comprehensive comparison involving DDMs, including support vector machine (SVM), gene expression programming (GEP), multilayer perceptron (MLP), gradient boosting trees (GBT) and multivariate adaptive regression spline (MARS) models, against two regression equations for predicting scour depth around cylindrical bridge piers. Evaluation employs statistical indices, such as root-mean-square error (RMSE), coefficient of determination (R2), mean average error (MAE) and normalized discrepancy ratio (S(DDRmax)), to assess their predictive performance. A total of 455 datasets from previous research papers are employed for assessment. Dimensionless parameters Froude number $$\\left( {Fr = \\frac{U}{{\\sqrt {gy} }}} \\right)$$ F r = U gy , Pier Froude number $$Fr_{P} = \\frac{U}{{\\sqrt {g^{\\prime } D} }}$$ F r P = U g ′ D , and the ratio of scour depth to pier diameter $$(\\frac{\\text{y}}{{\\text{D}}})$$ ( y D ) are carefully selected as influential model inputs through dimensional analysis and the gamma test. The results highlight the superior performance of the SVM model. In the training phase, it exhibits an RMSE of 0.1009, MAE of 0.0726, R2 of 0.9401, and SDDR of 2.9237. During testing, the SVM model shows an RMSE of 0.023, MAE of 0.017, R2 of 0.984, and SDDR of 5.301. Additionally, it has an average error of − 0.065 and a total error of − 20.642 in the training set and an average error of − 0.005 and a total error of − 0.707 in the testing set. Conversely, the M5 model exhibits the lowest accuracy. The statistical metrics unequivocally establish the SVM model as significantly outperforming the experimental models, placing it in a higher echelon of predictive accuracy.
Mahdi Majedi-Asl, Mehdi Fuladipanah, Hedi Mahmoudpour, Ebrahim Ebrahimpour, and Ozgur Kisi
Informa UK Limited
Mahdi Majedi-Asl, Mehdi Fuladipanah, Venkat Arun, and Ravi Prakash Tripathi
IWA Publishing
Abstract As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the support vector machine (SVM) and the gene expression programming (GEP) algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively.
Mehdi Fuladipanah and Ali Makvandi
Politechnika Wroclawska Oficyna Wydawnicza
Accurate determination of mean annual sediment load (MASL) of natural rivers will affect administrative aspects of water planning in dams. The MASL in Dez River in the southwest Iran has been considered. Sezar and Bakhtiari are its two branches. The amount of MASL was predicted by the USBR equation with three scenarios: using mean value of sediment discharge, using probabilistic classification of river flow data and using separation of wet and dry months. The results show that the USBR equation can be used to evaluate MASL in the Dez basin.