Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior

Etemad‐Shahidi, Amir; Mahjoobi, J.

doi:10.1016/j.oceaneng.2009.08.008

Cited by 182 publications

(66 citation statements)

References 32 publications

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“…Contudo, não foram encontrados estudos na literatura especializada que utilizaram essa técnica para estimativa de variáveis dendrométricas, porém, quando aplicadas em outras áreas, como modelagem do processo chuva-vazão (Solomatine & Dulal, 2003;Etemad-Shahidi & Mahjoobi, 2009), de sedimentos (Bhattacharya & Solomatine, 2006) e evapotranspiração (Rahimikhoob, 2014), o desempenho foi similar ao das RNAs.…”

Section: Idade (Anos) N Dap (Cm) (Cv %) Ht (M) (Cv %)unclassified

Estimativa do Afilamento do Fuste de Araucária Utilizando Técnicas de Inteligência Artificial

et al. 2017

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RESUMOO objetivo deste estudo foi comparar o desempenho de técnicas de inteligência artificial com funções de afilamento e avaliar a contribuição da idade nessa estimativa. O conjunto de dados foi composto por 135 observações, abrangendo as idades de 6, 12, 18, 24 e 43 anos de um povoamento de Araucaria angustifolia. As funções de afilamento ajustadas foram Kozak modificado, Schöepfer, Hradetzky e Garay. Os modelos de inteligência artificial utilizados foram: RNA e árvore modelo (M5P). Os vetores de entrada foram as mesmas variáveis utilizadas nas equações de afilamento e também o mesmo arranjo com a adição da idade. Foram utilizados 70% dos dados para ajuste e 30% para validação. A função de afilamento de Hradetzky forneceu o melhor ajuste. Dentre os modelos avaliados, a RNA propiciou as melhores estimativas, com destaque para a RNA com adição da variável idade. O desempenho da M5P foi satisfatório, porém, inferior às demais técnicas utilizadas.Palavras-chave: função de afilamento, M5P, multi layer perceptron. Araucaria Stem Taper or Use of Artificial Intelligence Techniques ABSTRACTThe aim of this study was to compare the performance of artificial intelligence techniques with taper functions and evaluate the effect of age on this estimate. The data set was comprised of 135 observations covering the ages 6, 12, 18, 24 and 43 years of age of a stand of Araucaria angustifolia. Adjusted taper functions were Kozak Schöepfer, Hradetzky and Garay modified. The artificial intelligence models used were: ANN and tree model. The input vectors are the same variables used in taper equations and also the same arrangement with the addition of age. 70% of the data was used for adjustment and 30% for validation. The taper function Hradetzky provided the best fit. Among the models evaluated, the ANN provided the best estimates, highlighting the ANN by adding the age variable. The performance of M5P was satisfactory, however, less effective than the other techniques.

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Section: Idade (Anos) N Dap (Cm) (Cv %) Ht (M) (Cv %)unclassified

Estimativa do Afilamento do Fuste de Araucária Utilizando Técnicas de Inteligência Artificial

et al. 2017

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“…We choose M5Prime as the relevance score predictor because it has shown excellent performance in similar tasks [3]. The M5Prime is one kind of regression-trees, but it is not constrained by using M5Prime as the regression-tree model.…”

Section: Poi Recommendationmentioning

confidence: 99%

“…While numerical value prediction techniques have been developed for many applications, such as demographic prediction [4], bio life cycle analysis [10] and prediction of geographical natural [3], the problem has not been explored previously under the context of urban computing. Furthermore, the context-aware and environmental information changes quickly especially in urban areas.…”

Section: Introductionmentioning

confidence: 99%

Urban point-of-interest recommendation by mining user check-in behaviors

Ying

Kuo

et al. 2012

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

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In recent years, researches on recommendation of urban PointsOf-Interest (POI), such as restaurants, based on social information have attracted a lot of attention. Although a number of socialbased recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. It leads to that the recommended POIs list is usually constrained within the users' or friends' living area. Furthermore, since context-aware and environmental information changes quickly, especially in urban areas, how to extract appropriate features from such kind of heterogeneous data to facilitate the recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Urban POI-Mine (UPOI-Mine) that integrates location-based social networks (LBSNs) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space, so as to support the prediction of interestingness of POI related to each user's preference. Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Gowalla, the proposed UPOIMine is shown to deliver excellent performance.

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“…For example, Londhe [16] used artificial neural networks (ANNs) and genetic programming for the estimation of missing wave heights in the eastern Gulf of Mexico. Etemad-Shahidi and Mahjoobi [17] used model trees (M5) for the same purpose, and the results were compared with those of ANNs. Tsai and Tsai [18] established a backpropagation (BP) neural network model to predict four surface waves by using acoustic wave and pressure data measured by ultrasonic wave gauges.…”

Section: Introductionmentioning

confidence: 99%

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Wei

2017

Energies

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Seasonal typhoons provide energy into the wave field in summer and autumn in Taiwan. Typhoons lead to abundant wave energy near the coastal area and cause storm surges that can destroy offshore facilities. The potential for wave energy can be obtained from analyzing the wave height. To develop an effective model for predicting typhoon-induced wave height near coastal areas, this study employed various popular data mining models-namely k-nearest neighbors (kNN), linear regressions (LR), model trees (M5), multilayer perceptron (MLP) neural network, and support vector regression (SVR) algorithms-as forecasting techniques. The principal component analysis (PCA) was then performed to reduce the potential variables from the original data at the first stage of data preprocessing. The experimental site was the Longdong buoy off the northeastern coast of Taiwan. Data on typhoons that occurred during 2002-2011 and 2012-2013 were collected for training and testing, respectively. This study designed four PCA cases, namely EV1, TV90, TV95, and ORI: EV1 used eigenvalues higher than 1.0 as principal components; TV90 and TV95 used the total variance percentages of 90% and 95%, respectively; and ORI used the original data. The forecast horizons varying from 1 h to 6 h were evaluated. The results show that (1) in the PCA model' cases, when the number of attributes decreases, computing time decreases and prediction error increases; (2) regarding classified wave heights, M5 provides excellent outcomes at the small wavelet wavelet level; MLP has favorable outcomes at the large wavelet and small/moderate wave levels; meanwhile, SVR gives optimal outcomes at the long wave and high/very high wave levels; and (3) for performance of lead times, MLP and SVR achieve more favorable relative weighted performance without consideration of computational complexity; however, MLP and SVR might obtain lower performance when computational complexity is considered.

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Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior

Cited by 182 publications

References 32 publications

Estimativa do Afilamento do Fuste de Araucária Utilizando Técnicas de Inteligência Artificial

Estimativa do Afilamento do Fuste de Araucária Utilizando Técnicas de Inteligência Artificial

Urban point-of-interest recommendation by mining user check-in behaviors

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

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