Prediction of Stability Number for Tetrapod Armour Block Using Artificial Neural Network and M5' Model Tree

Reference evapotranspiration (ETO) is one of the major parameters affecting hydrological cycle. Use of satellite images can be very helpful to compensate for lack of reliable weather data. This study aimed to determine ETO using land surface temperature (LST) data acquired from MODIS sensor. LST data were considered as inputs of two data-driven models including artificial neural network (ANN) and M5 model tree to estimate ETO values and their results were compared with calculated ETO by FAO-Penman-Monteith (FAO-PM) equation. Climatic data of five weather stations in Khuzestan province, which is located in the southeastern Iran, were employed in order to calculate ETO. LST data extracted from corresponding points of MODIS images were used in training of ANN and M5 model tree. Among study stations, three stations (Amirkabir, Farabi, and Gazali) were selected for creating the models and two stations (Khazaei and Shoeybie) for testing. In Khazaei station, the coefficient of determination (R2) values for comparison between calculated ETO by FAO-PM and estimated ETO by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, R2 values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate ETO by means of LST data derived from MODIS sensor.

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An easy way to use artificial neural network model for calculating stability number of rock armors

Ocean Engineering ◽

10.1016/j.oceaneng.2016.10.013 ◽

2016 ◽

Vol 127 ◽

pp. 349-356 ◽

Cited By ~ 6

Author(s):

Anzy Lee ◽

Sung Eun Kim ◽

Kyung-Duck Suh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Stability Number ◽

Artificial Neural

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A comparative study of concrete strength prediction using artificial neural network, multigene programming and model tree

Challenge Journal of Structural Mechanics ◽

10.20528/cjsmec.2019.02.002 ◽

2019 ◽

Vol 5 (2) ◽

pp. 42

Author(s):

Preeti Kulkarni ◽

Shreenivas N. Londhe ◽

Pradnya R. Dixit

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Concrete Strength ◽

Model Performance ◽

Recycled Aggregate Concrete ◽

Recycled Aggregate ◽

Model Tree ◽

Multigene Genetic Programming ◽

Input Parameters ◽

Artificial Neural

In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing model/s to predict strength of concrete with an acceptable performance.

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Modelización y análisis de la calidad del aire en la ciudad de Oviedo (norte de España), mediante los enfoques PSO-SVM, red neuronal MLP y árbol de regresión M5 = Modeling and analysis of the air quality in the city of Oviedo (Northern Spain) using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree

10.18002/10612/7953 ◽

2018 ◽

Author(s):

Alejandro Aurelio Rodríguez Miranda

Keyword(s):

Neural Network ◽

Air Quality ◽

Northern Spain ◽

M5 Model Tree ◽

Modeling And Analysis ◽

Mlp Neural Network ◽

Model Tree ◽

The City ◽

M5 Model

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An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration

Theoretical and Applied Climatology ◽

10.1007/s00704-015-1582-z ◽

2015 ◽

Vol 126 (3-4) ◽

pp. 413-425 ◽

Cited By ~ 14

Author(s):

Ozgur Kisi ◽

Yasin Kilic

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Reference Evapotranspiration ◽

M5 Model Tree ◽

Generalization Ability ◽

Model Tree ◽

Artificial Neural ◽

M5 Model

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Modeling pressure drop produced by different filtering media in microirrigation sand filters using the hybrid ABC-MARS-based approach, MLP neural network and M5 model tree

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2017.05.008 ◽

2017 ◽

Vol 139 ◽

pp. 65-74 ◽

Cited By ~ 8

Author(s):

P.J. García Nieto ◽

E. García-Gonzalo ◽

J. Bové ◽

G. Arbat ◽

M. Duran-Ros ◽

...

Keyword(s):

Neural Network ◽

Pressure Drop ◽

M5 Model Tree ◽

Sand Filters ◽

Mlp Neural Network ◽

Model Tree ◽

M5 Model

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ÁRVORE MODELO FRENTE A UMA REDE NEURAL ARTIFICIAL PARA A MODELAGEM CHUVA-VAZÃO

Nativa ◽

10.31413/nativa.v7i5.7089 ◽

2019 ◽

Vol 7 (5) ◽

pp. 527

Author(s):

Aline Bernarda Debastiani ◽

Sílvio Luís Rafaeli Neto ◽

Ricardo Dalagnol da Silva

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Tree ◽

Superior Performance ◽

Multi Layer Perceptron ◽

Rainfall Runoff ◽

Model Tree ◽

Runoff Modeling ◽

Artificial Neural ◽

Artificial Neural Network Ann

O objetivo deste estudo é investigar o desempenho da árvore modelo (M5P) e sua sensibilidade à poda e comparação com o desempenho de uma Rede Neural Artificial (RNA) para a simulação da vazão média diária mensal. A motivação para esta análise está na maior simplicidade e velocidade de processamento da M5P comparado às RNAs e a carência de estudos aplicando este método na modelagem hidrológica. O estudo foi desenvolvido na bacia hidrográfica do Alto Canoas, tendo um delineamento experimental composto por um período de treinamento, um de validação cruzada e dois períodos de testes. A RNA utilizada foi a Multi Layer Perceptron (MLP), implementada no software MATLAB, e a M5P (com e sem poda), disponível do software WEKA. O algoritmo M5P se mostrou sensível à poda em somente metade dos tratamentos. A M5P apresentou bom ajuste na modelagem, porém a RNA apresentou desempenho superior em todos os tratamentos.Palavras-chave: rede neural artificial; árvore de regressão; Bacia do Alto Canoas. MODEL TREE IN COMPARISON TO ARTIFICIAL NEURAL NETWORK FOR RAINFALL-RUNOFF MODELING ABSTRACT: The aim of this study is to investigate the performance of the model tree (M5P) and its sensitivity to pruning and comparison to the performance of an Artificial Neural network (ANN) for the simulation of daily average discharge of the month. The motivation for this analysis is on simplicity and speed of processing M5P compared the RNAs. The study was developed in the Alto Canoas watershed, having an experiment consisting of a training period, a cross-validation and two testing periods. The ANN used was the Multi Layer Perceptron (MLP), implemented in MATLAB software, and M5P (with and without pruning), available from the WEKA software. M5P algorithm proved sensitive to pruning in half of the treatments. The M5P showed good fit in the modeling, but the RNA presented superior performance in all treatments.Keywords: artificial neural network; regression tree; Basin Alto Canoas.

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