Novel approach for dam break flow modeling using computational intelligence

Seyedashraf, Omid; Mehrabi, Mohammad; Akhtari, Ali Akbar

doi:10.1016/j.jhydrol.2018.03.001

Cited by 71 publications

(36 citation statements)

References 32 publications

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“…An artificial neural network (ANN) mimics the human neural system [32] to make an intelligent processor capable of being used for many regression and classification aims with complicated non-linear conditions [33]. The high prediction capability of the ANN has made its different notions popular for various engineering simulations [34][35][36][37][38]. Multi-layer perceptron (MLP) is known as the most common type of ANN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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“…In this study, the activation function (f(x)) is selected to be Tan-sigmoid (Tansig), due to its satisfying performance in previous studies (Seyedashraf et al 2018). This function is expressed as follows:…”

Section: Multilayer Perceptronmentioning

confidence: 99%

A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

Li²,

Moayedi

et al. 2019

Geomatics, Natural Hazards and Risk

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The focal point of this study is to assess the efficacy of a state-ofthe-art optimization technique namely, particle swarm optimization (PSO) for enhancing the performance of the artificial neural network (ANN) in modeling the seismic landslides at Ludian districts, China. Twelve geological and hydrological landslide-conditioning factors namely, elevation, lithology, slope degree, slope aspect, stream power index, peak ground acceleration, topographic wetness index, distance to river, distance to road, distance to fault, normalized difference vegetation index and plan curvature were considered within a geographic information system (GIS). After achieving the optimal structure of the multilayer perceptron neural network, the PSO algorithm was applied to improve its efficiency. The landslide susceptibility maps were generated in a GIS environment and area under the curve (AUC) criterion was used to assess the integrity of employed predictive models. The results showed that after applying the PSO algorithm, AUC experiences a significant increase from 0.765 to 0.825 in the validation phase. Moreover, respective AUCs of 0.812 and 0.828 obtained for the training phase of ANN and PSO-ANN reveal the efficiency of the proposed algorithm in improving the ANN accuracy. ARTICLE HISTORY

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“…Similar to many other fields of research, AI techniques have attracted great attention in building energy consumption evaluation. Among various types of AI techniques, artificial neural network (ANN) is a well-known technique that is widely employed for many simulation problems [20,21]. Moreover, ANN has emerged as an effective tool in building energy management [22,23].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

et al. 2019

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Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.

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Novel approach for dam break flow modeling using computational intelligence

Cited by 71 publications

References 32 publications

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

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