Mehdi Fuladipanah

@iauramhormoz.ac.ir

Department of Civil Engineering, Ramhormoz Branch
Islamic Azad University, Ramhormoz, Iran

Mehdi Fuladipanah


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https://researchid.co/fuladipanah

RESEARCH INTERESTS

Hydraulics, River engineering, sediment, hydrodynamics, artificial intelligent
23

Scopus Publications

Scopus Publications

  • Evaluating energy dissipation in through gabion sills under free and submerged hydraulic jump conditions using numerical simulation
    Mohsen Najarchi, Mehdi Fuladipanah, Mahmood Rabani Bidgoli
    Results in Engineering, 2026
    • Advanced Modeling of Hydraulic Jumps : The study uses the FLOW3D model to accurately simulate energy dissipation in hydraulic jumps over gabion sills, demonstrating the model's capability in capturing complex flow dynamics under both free and submerged conditions. • Optimized Performance Metrics : Through rigorous calibration, the study achieves high accuracy, with error metrics such as RMSE as low as 0.0147 and R² values exceeding 0.99, validating model's predictions against experimental data. • Key Insights on Energy Dissipation : The research identifies the influence of gabion granulation, sill height, and positioning on energy dissipation, with findings showing up to 70% dissipation in free jumps and up to 60% in submerged conditions under optimal configurations. This study applies the FLOW3D numerical simulator to predict energy dissipation across gabion sills in free and submerged hydraulic jump conditions downstream of an ogee spillway. FLOW3D was rigorously calibrated and verified, showing high accuracy in modeling complex flow dynamics. The model’s performance was optimized by selecting the M7 mesh configuration and the RNG (k-ε) turbulence model, achieving error metrics that align closely with experimental data: RMSE of 0.0147 and MAE of 0.0092 with R² = 0.993 for free jumps, and RMSE of 0.0236 and MAE of 0.0155 with R²=0.9959 for submerged conditions. Simulated predictions indicated maximum energy dissipation of 70% under free jump conditions with fine gabion granulation, decreasing to 50% with coarser materials. For submerged jumps, energy loss predictions reached 60% at an optimal sill height and position, with the model accurately reflecting decreased dissipation as gabion size increased. FLOW3D’s robust performance and high correlation with empirical findings underscore its potential as a reliable tool for modeling hydraulic jumps and optimizing energy dissipation designs in hydraulic structures.
  • Machine learning-based discharge coefficient estimation in trapezoidal-arched labyrinth weirs
    Mohammad Heidarnejad, Jamal Feili, Mehdi Fuladipanah, Upaka Rathnayake
    Asian Journal of Water Environment and Pollution, 2025
    Weirs represent a frequently employed mechanism for regulating water surface elevations and managing flow within canals and hydraulic infrastructures. Among these, labyrinth weirs constitute a distinctive variant capable of accommodating a specific discharge while maintaining a reduced upstream water level compared to conventional linear weirs. The present investigation delved into the evaluation of the effectiveness of multilayer perceptron (MLP) networks, support vector machine (SVM), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), aiming to predict the discharge coefficient (Cd) of a trapezoidal-arched labyrinth weir with an expanded central cycle. A dataset including 108 laboratory observations was utilized. The dimensionless parameters were obtained from the parameters including inside apex width of the middle cycle (w1), inside apex width of the end cycles (w2), weir height on the upstream side (B), unsubmerged total upstream head on the weir (Hd), and gravitational acceleration (g). The model was developed with the dimensionless parameters and Cd. Root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and developed discrepancy ratio (DDR) were used as performance assessment criteria. Based on these metrics, all four models exhibited the latent capacity to predict the Cd value. However, the MLP model demonstrated superior performance among the models during both training (RMSE = 0.024, MAE = 0.020, R2 = 0.816, and Cd[DDRmax] = 8.07) and testing (RMSE = 0.011, MAE = 0.006, R2 = 0.688, and Cd[DDRmax] = 11.32) phases. Sequentially, the subsequent standings were secured by the SVM, GEP, and MARS. MLP outperformed SVM, GEP, and MARS models in predicting Cd, achieving the highest R² and lowest RMSE/MAE values.
  • Machine Learning Approaches for Simulating Temporal Changes in Bed Profiles Around Cylindrical Bridge Pier: A Comparative Analysis
    Ahad Molavi, Fariborz Ahmadzadeh Kaleybar, Namal Rathnayake, Upaka Rathnayake, Mehdi Fuladipanah, Hazi Mohammad Azamathulla
    Hydrology, 2025
    Submerged vanes offer a promising solution for reducing scour depth around hydraulic structures such as bridge piers by modifying near-bed flow patterns. However, temporal changes in bed profiles around a cylindrical pier remain insufficiently quantified. This study employs three machine learning models (MLMs), gene expression programming (GEP), support vector regression (SVR), and an artificial neural network (ANN), to simulate the temporal evolution of the bed profile around a cylindrical pier under constant subcritical flow. We use a published laboratory flume dataset (106 observations) obtained for a pier of diameter D=6cm and uniform sediment with median size D50=0.43mm. Geometric/layout parameters of the submerged vanes (number n, transverse offset z, longitudinal spacing e, and distance from the pier base a) were fixed at their reported optima, and subsequent tests varied installation angles α to minimize scour. Models were trained on 70% of the data and tested on 30% using dimensionless inputs (t/te,α1,α2,α3) with t the elapsed time from the start of the run and te the equilibrium time at which scour growth becomes negligible and response s/D with s the instantaneous scour depth at time t. The GEP model with a three-gene structure achieved the best accuracy. During training and testing, GEP attained (RMSE, MAE, R2, (Ds/D)DDR(max))=(0.0864,0.0681,0.9237,4.25) and (0.0729,0.0641,0.9143,4.94), respectively, where Ds denotes scour depth at equilibrium state, D is the pier diameter, and DDR(max)≡max(Ds/D) is the maximum dimensionless depth ratio observed/predicted.
  • Assessing long-term groundwater level trends in Karakalpakstan using non-parametric statistical methods
    Mehdi Fuladipanah, Kenjabek Rozumbetov, Namal Rathnayake, Valery Erkudov, Mirzohid Koriyev, Upaka Rathnayake
    Asian Journal of Water Environment and Pollution, 2025
    Climate change has significantly impacted global hydrometeorological variables, placing increasing stress on groundwater resources. This study investigates long-term groundwater level trends in the Republic of Karakalpakstan, Uzbekistan, using a combination of non-parametric statistical models. The Mann–Kendall test, Spearman’s rank correlation, and innovative polygon trend analysis (IPTA) were applied to assess spatiotemporal variations. To address the limitations of parametric methods, this study utilizes robust, assumption-free trend detection techniques. The results reveal statistically significant increasing trends in groundwater levels across most provinces, particularly in Muynak (Z=3.884, p<0.001) and Republic-wide (Z=3.603, p<0.001). In contrast, provinces such as Turtkul, Ellikkala, and Nukus exhibit no significant trends. The IPTA method highlights seasonal fluctuations, with notable decreases in specific months despite the overall upward trend. These findings emphasize the need for localized groundwater management strategies that consider both seasonal dynamics and long-term changes. By integrating multiple statistical techniques, this study provides a comprehensive evaluation of groundwater variability and offers valuable insights for policymakers and water resource managers in arid regions facing climate-induced water challenges.
  • Artificial Intelligence for Hydraulic Engineering: Predicting discharge coefficients in trapezoidal side weirs
    Mehdi Fuladipanah, , Saleema Panda, Namal Rathnayake, Upaka Rathnayake, Hazi Md. Azamathulla, Yukinobu Hoshino, , , , and
    Mathematical Biosciences and Engineering, 2025
    Accurately predicting the discharge coefficient (Cd) is fundamental to the hydraulic design and performance of side weirs. In this study, we introduced a novel artificial intelligence (AI) framework to enhance the prediction accuracy of Cd for two-cycle trapezoidal labyrinth side weirs. Using a comprehensive laboratory dataset, three distinct machine learning models (MLMs), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Gene Expression Programming (GEP), were developed and rigorously compared with application of the Γ-test technique for sensitivity analysis, systematically identifying the five most influential geometric and hydraulic parameters (Fr, $ \frac{\text{L}}{\text{B}} $, $ \frac{{\text{L}}_{\text{e}}}{\text{L}} $, $ \frac{{\text{Y}}_{\text{1}}\text{-P}}{\text{P}} $, α) to serve as model inputs. The model's efficacy was evaluated across training, testing, and validation phases using multiple statistical metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and the Maximum Developed Discrepancy Ratio (Cd(DDRmax)). The results demonstrated that the three MLMs are effective predictive tools. However, the ANN model, specifically an MLP5-7-1 architecture utilizing Atan and Identity activation functions optimized with the BFGS 385 algorithm, significantly outperformed the others. It achieved superior results (e.g., validation phase: RMSE = 0.0061, MAE = 0.0003, R2 = 0.9301, Cd(DDRmax) = 5.22), confirming its highest predictive accuracy and robustness. This research conclusively shows that MLMs, particularly ANN, offer a highly precise and efficient method for predicting Cd in complex hydraulic structures.
  • Enhancing river flow predictions: Comparative analysis of machine learning approaches in modeling stage-discharge relationship
    Ozgur Kisi, Hazi Mohammad Azamathulla, Fatih Cevat, Christoph Kulls, Mehdi Kuhdaragh, Mehdi Fuladipanah
    Results in Engineering, 2024
    Streamflow, a pivotal variable in water resources management, holds profound significance in shaping the decision-making processes of hydrologic projects. This paper tries to delve into the exploration of the stage-discharge relationship using three machine learning methods (MLMs) namely multi-layer neural networks (MLNN), radial basis neural networks (RBNN), and neuro-fuzzy systems (ANFIS) to predict and simulate mean daily stage-discharge data derived from two monitoring stations, Bulakbasi and Karaozü, Kizilirmak River, Turkey. Root mean square error (RMSE), Mean absolute percentage error (MAPE), coefficient of determination (R2), and the Developed Discrepancy Ratio (DDR) metrics were utilized to MLMs' performance assessment. The performance evaluation indices (RMSE, MAEP, R2, DDR) for the preeminent MLNN model applied to Bulakhbashi and Karasu stations were determined as (0.29, 1.57, 0.9998, 17.62) and (1.71, 6.56, 0.9980, 6.65), respectively. The MLNN model contributed to a notable enhancement in the RMSE performance index for the aforementioned stations, exhibiting improvements of 87% and 56%, respectively. These results affirm the MLNN's proficiency in accurately capturing the stage-discharge at both monitoring stations.
  • In-depth simulation of rainfall–runoff relationships using machine learning methods
    Mehdi Fuladipanah, Alireza Shahhosseini, Namal Rathnayake, Hazi Md. Azamathulla, Upaka Rathnayake, D. P. P. Meddage, Kiran Tota-Maharaj
    Water Practice and Technology, 2024
    Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditions, plays a distinctive and pivotal role in decision-making within water resource management. The significance of machine learning models (MLMs) has become pronounced in addressing these issues. In this context, the forthcoming research endeavors to model the RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, and Multivariate Adaptive Regression Splines (MARS). The simulation was conducted within the Malwathu Oya watershed, employing a dataset comprising 4,765 daily observations spanning from July 18, 2005, to September 30, 2018, gathered from rainfall stations, and Kappachichiya hydrometric station. Of all input combinations, the model incorporating the input parameters Qt−1, Qt−2, and R̄t was identified as the optimal configuration among the considered alternatives. The models' performance was assessed through root mean square error (RMSE), mean average error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The GEP model emerged as the superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) of (43.028, 9.991, 0.909, 0.736) during the training process and (40.561, 10.565, 0.832, 1.038) during the testing process.
  • Quantitative forecasting of bed sediment load in river engineering: an investigation into machine learning methodologies for complex phenomena
    Mehdi Fuladipanah, H. Md. Azamathulla, Ozgur Kisi, Mehdi Kouhdaragh, Vishwandham Mandala
    Water Supply, 2024
    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.
  • An in-depth comparative analysis of data-driven and classic regression models for scour depth prediction around cylindrical bridge piers
    Mehdi Fuladipanah, Mohammad Azamathulla Hazi, Ozgur Kisi
    Applied Water Science, 2023
    The 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.
  • Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models
    Mehdi Fuladipanah, H Md Azamathulla, Kiran Tota-Maharaj, Vishwanadham Mandala, Aaron Chadee
    Results in Engineering, 2023
    Flip-bucket spillways are utilized in hydraulic engineering to diminish the kinetic energy of flowing water by redirecting the flow jet into the air. In the downstream stailing basin with low tail-water, sediment particles movement results in scour hole formation, posing a threat to spillway stability. The accurate prediction of scour hole depth is a crucial area of the present research work. This study endeavors to employ four data-driven models (DDMs), namely Support Vector Machine (SVM), Gene Expression Programming (GEP), Multilayer Perceptron (MLP), and Multivariate Adaptive Regression Splines (MARS), in combination with five selected empirical equations. The objective is to accurately predict scour depth utilizing field-collected data from site number 84. Relative scour depth, dsH1, was simulated based on the readily extracted parameter i.e. Froude number, Fr=qgH13. The evaluation of model performance was conducted using fundamental metrics, including root mean square error (RMSE), coefficient of determination (R2), mean average error (MAE), and the maximum value of the developed discrepancy ratio (DDRmax). Among the DDMs, the MARS model demonstrated superior performance in both the training and testing phases. In the training phase, it yielded metrics (RMSE = 0.08665, MAE = 0.05714, R2 = 0.99169, DDRmax = 4.519), and in the testing phase, it produced metrics (RMSE = 0.0252, MAE = 0.0170, R2 = 0.09933, DDRmax = 9.144). This exceptional performance of the MARS model surpassed the initially selected (Wu, 1973) [1] experimental model, which exhibited metrics (RMSE = 0.39667, MAE = 0.17463, R2 = 0.96172, DDR = 1.428). The evaluation indices conclusively establish the MARS method's absolute superiority over the experimental approach proposed by Wu (1973) [1].
  • Optimization design of quality monitoring network of Urmia plain using genetic algorithm and vulnerability map
    Mahdi Majedi-Asl, Mehdi Fuladipanah, Hedi Mahmoudpour, Ebrahim Ebrahimpour, Ozgur Kisi
    Geocarto International, 2023
  • Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs
    Mahdi Majedi-Asl, Mehdi Fuladipanah, Venkat Arun, Ravi Prakash Tripathi
    Water Supply, 2022
  • Simulation of bridge pier scour depth base on geometric characteristics and field data using support vector machine algorithm
    Mahdi Majedi-Asl, R. Daneshfaraz, M. Fuladipanah, J. Abraham, M. Bagherzadeh
    Journal of Applied Research in Water and Wastewater, 2020
  • Hydrodynamic of river flow during a drought event (case study, Karoon River, Khuzestan, Iran)
    Ecology Environment and Conservation, 2016
  • Neural network for estimation of scour hole dimensions downstream of siphon spillway
    Advances in Environmental Biology, 2014
  • Developing unit hydrograph using dark model (Case study: Poledoab catchment, Iran)
    Ecology Environment and Conservation, 2014
  • Suspended load routing using artificial neural network and 1D fully coupled model (Case study: Ahwaz station, Karoon, Iran)
    Ecology Environment and Conservation, 2014
  • Hydrological based environmental flow assessment methods (Case study: Gharasou River, Ardabil)
    Ecology Environment and Conservation, 2014
  • Application of the usbr equation for surveying balance of sediment yield in dez river branches in Iran
    Mehdi Fuladipanah, Ali Makvandi
    Environment Protection Engineering, 2013
  • Sensitivity analysis of one dimensional hydrodynamic fully coupled model
    Middle East Journal of Scientific Research, 2012
  • The determination of nonlinear equations to predict scour hole dimensions downstream of siphon spillway
    World Applied Sciences Journal, 2012
  • The estimation of snowmelt runoff using SRM case study (Gharasoo basin, Iran)
    World Applied Sciences Journal, 2012
  • The derivation of energy dissipation equation for adverse-slopped stepped spillway
    World Applied Sciences Journal, 2011