Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System

Roshni, Thendiyath; Jha, Madan K.; Deo, Ravinesh C.; Vandana, A.

doi:10.1007/s11269-019-02253-4

Cited by 39 publications

(12 citation statements)

References 33 publications

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“…al. has developed a Feedforward Artificial Neural Network (FFANN) and the hybrid WANN model to model spatio-temporal fluctuations in groundwater levels [17]. The efficiency of these hybrid models is calculated using goodness-of-fit.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Method for Rainfall Prediction and Classification using Neural Networks

Rajkumar¹,

Subrahmanyam²

2021

IJACSA

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In the field of food production, it is an important and difficult job to maintain water sources for major population centres and reduce the risk of flooding, to forecast rainfall reliably and accurately. Accurate and genuine forecasts of rainfall on monthly and seasonal time scales help to provide beneficiaries with knowledge on the control of water supplies, farm forecasting and integrated crop insurance applications. Present rainfall prediction is the challenging task for the researchers and most of the rainfall prediction techniques are fail in accuracy. For this we propose a new effective hybrid approach for forecasting and classifying rainfall using the neural network and ACO method. The collected rainfall data were preprocessed by filling missing data and normalized by min-max normalization, the processed data is given to various classifiers for evaluating its performance. The performance of the existing and proposed models is compared. Performance comparison of existing feed-forward, cascade-forward and pattern recognition NN classifier and the proposed ACO+feed-forward backpropagation, ACO+ cascade-forward backpropagation and ACO+ pattern recognition NN classifier are done. The entire HNN forecasting protocol consists of pre-processing and choosing the input vector and maximising the number of hidden nodes using ACO and ANN modelling.

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Section: Literature Surveymentioning

confidence: 99%

A Novel Method for Rainfall Prediction and Classification using Neural Networks

Rajkumar¹,

Subrahmanyam²

2021

IJACSA

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“…It is not worth noticing that the architecture of the FNN model is very important. Depending on the problem of interest, the prediction results could exhibit significant variation from using one architecture to another [96,107,108]. As the numbers of inputs and outputs are fixed, the undetermined parameters of the architecture are the number of hidden layer(s) and the number of neurons in each hidden layer(s) [109].…”

Section: Optimization Of Weight Parameters Of Fnn Using the Iwo Technmentioning

confidence: 99%

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

Nguyen

Tran

et al. 2020

Materials

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Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (P u ) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict P u was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN-IWO model and improve its prediction performance. The results showed that the FNN-IWO algorithm is an excellent predictor of P u , with a value of R 2 of up to 0.979. The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R 2 , respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P u in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.Materials 2020, 13, 1205 2 of 25 larger energy absorption capacity [7], convenient construction [11], economy of materials [12][13][14], and excellent seismic and refractory performance [15]. In particular, this type of structure can reduce the environmental burden by removing formwork [16], reusing steel pipes, and using high quality concrete with recycled aggregate [17]. The characteristics of CFST are that the steel material is located far from the central axis so the rigidity of the column is very large, and thus it also contributes to increasing the moment of inertia of the structure [5,18]. The ideal form of concrete core works against the compressive load and hinders the local buckling state of the steel pipe. Therefore, the CFST structures are often used in locations subject to large compressive loads [9,15,19]. The CFST columns are mainly divided into square columns, round columns, and rectangular columns, based on different cross-sectional forms [15]. In particular, the square and rectangular CFST columns have the advantage of easy connection and reliable work with other structural members such as beams, walls, and panels [20]. Compared with square CFST columns, rectangular columns have irregular bending stiffness along different axes, so this type of column is suitable for the mechanical behavior of members including arch ribs, pillars, abutments, and piers, and other structural members under load actions vary greatly from vertical to horizontal [6]. Because the scope of application of rectangular CFST columns is quite wide and this column is mainly subjected to compression, the main purpose of the paper is to analyze and evaluate the ultimate bearing capacity of...

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“…As demonstrated in the literature, the capability of an NN model depends highly on the selected architecture [124,[155][156][157][158]. Therefore, the determination of an appropriate architecture is required to study an NN model.…”

Section: Construction Of the Hybrid Models (Nn-rcga And Nn-ffa)mentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System

Cited by 39 publications

References 33 publications

A Novel Method for Rainfall Prediction and Classification using Neural Networks

A Novel Method for Rainfall Prediction and Classification using Neural Networks

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

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