Browsing by Author "Shadkani, Sadra"

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Citation - WoS: 8
Citation - Scopus: 9
A novel stabilized artificial neural network model enhanced by variational mode decomposing
(CELL PRESS, 2024) Ali Danandeh Mehr; Sadra Shadkani; Laith Abualigah; Mir Jafar Sadegh Safari; Hazem Migdady; Mehr, Ali Danandeh; Migdady, Hazem; Shadkani, Sadra; Safari, Mir Jafar Sadegh; Abualigah, Laith; Danandeh Mehr, Ali
Existing artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues we propose a stabilized ANNs called SANN. The SANN efficiently optimizes ANN structure via incorporation of an additional numeric parameter into every layer of the ANN. To exemplify the efficacy and efficiency of the proposed approach we provided two practical case studies involving meteorological drought forecasting at cities of Burdur and Isparta T & uuml,rkiye. To enhance SANN forecasting accuracy we further suggested the hybrid VMD-SANN that integrated variation mode decomposition (VMD) with SANN. To validate the new hybrid model we compared its results with those obtained from hybrid VMD-ANN and VMD-Radial Base Function (VMD-RBF) models. The results showed superiority of the VMD-SANN to its counterparts. Regarding Nash Sutcliffe Efficiency measure the VMD-SANN achieves accurate forecasts as high as 0.945 and 0.980 in Burdur and Isparta cities respectively.
Citation - WoS: 22
Citation - Scopus: 24
Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods
(SPRINGER LONDON LTD, 2022) Saeed Samadianfard; Katayoun Kargar; Sadra Shadkani; Sajjad Hashemi; Akram Abbaspour; Mir Jafar Sadegh Safari; Kargar, Katayoun; Samadianfard, Saeed; Shadkani, Sadra; Safari, Mir Jafar Sadegh; Hashemi, Sajjad; Abbaspour, Akram
Owing to the nonlinear and non-stationary nature of the suspended sediment transport in rivers suspended sediment concentration (SSC) modeling is a challenging task in environmental engineering. Investigation of SSC is of paramount importance in river morphology and hydraulic structures operation. To this end for SSC modeling first random forest (RF) and multi-layer perceptron (MLP) standalone models were developed and then they were optimized with genetic algorithm (GA) and stochastic gradient descent (SGD) to develop GA-MLP GA-RF SGD-MLP and SGD-RF hybrid models. Variety of input scenarios are implemented for SSC prediction to find the best input combination. The streamflow and SSC data collected from two stations of Minnesota and San Joaquin rivers respectively located at South Dakota and California are utilized in the current study. Accuracies of the developed models are examined by means of three performance criteria of correlation coefficient (CC) scattered index (SI) and Willmott's index of agreement (WI). A significant promotion in accuracy of hybrid models has been seen in contrast to their standalone counterparts. As can be deduced from the results GA-MLP-5 and GA-RF-5 models with CC of 0.950 and 0.944 SI of 0.290 and 0.308 and WI of 0.974 and 0.971 respectively were found as best models for prediction of SSC at Minnesota river. The developed SGD-MLP-5 and SGD-RF-5 models with CC of 0.900 and 0.901 SI of 0.339 and 0.339 and WI of 0.945 and 0.946 respectively gave accurate results at San Joaquin river. Through the application of SGD algorithm the adaptive learning rate epochs rho L1 and L2 were activated and presumed as 0.004 10 1 0.000009 and 0 respectively. The ExpRectifier was considered as san activation operation due to its better efficiency in comparison with its alternatives for predicting SSC in SGD-MLP model. According to the results the fifth scenario that incorporates SSCt-1 SSCt-2 Q(t) Q(t-1) and Q(t-2) were found superior for SSC modeling in the studied rivers. The recommended hybrid algorithms based on GA and SGD optimization algorithms are proposed as practical tools for solving complex environmental problems.