Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system

Khosravi, Ali; Koury, R.N.N.; Machado, Luiz; Pabón, Juan José García

doi:10.1016/j.seta.2018.01.001

Cited by 188 publications

(83 citation statements)

References 41 publications

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“…Artificial Neural Networks (ANN) based on data mining techniques is known as a popular artificial intelligent method used to solve many problems of real worlds such as modeling nitrate pollution of groundwater [58], prediction of wind speed and wind direction [59] and forecasting the blast-produced ground vibration [45]. Furthermore, ANN is widely used in landslide modeling and mapping previously [53,60].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

et al. 2019

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Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

et al. 2019

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“…Soft computing and machine learning approaches in the multivariate forecasting content include fuzzy logic (FL) inference, support vector machines (SVM), extreme learning machines (ELM) and neural networks (NN) [15]. Most of these concepts will be discussed in Section 4.2 and are compared with our proposed method.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy

Pirhooshyaran

Scheinberg

Snyder

2020

Energy

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This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications. We integrate derivative-free optimization with an ensemble of sequence-to-sequence networks and design a new resampling technique called additive resampling, which, along with Bootstrap aggregating (bagging) resampling, are applied to initialize the ensemble structure. Moreover, we explore the proposed framework performance on three renewable energy sources-wind, solar and ocean wave-and conduct several short-to long-term forecasts showing the superiority of the proposed method compared to numerous machine learning techniques. The findings indicate that the introduced method performs more accurately when the forecasting horizon becomes longer. In addition, we modify the framework for automated feature selection. The model represents a clear interpretation of the selected features. Furthermore, we investigate the effects of different environmental and marine factors on the wind speed and ocean output power, respectively, and report the selected features. Finally, we explore the online forecasting setting and illustrate that the model outperforms alternatives through different measurement errors.

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“…General RNNs are likely to cause the long-term dependency problem followed by the vanishing gradient problem. Mobile health data generate a large number of sequences as time progresses and are constructed based on LSTM [30][31][32][33], where the long-term dependency problem is ameliorated. An LSTM model uses a gate mechanism to resolve the vanishing gradient problem.…”

Section: Long Short-term Memory Recurrent Neural Network Modeling Formentioning

confidence: 99%

Prediction Model of User Physical Activity using Data Characteristics-based Long Short-term Memory Recurrent Neural Networks

Kim¹,

Chung²

2019

KSII TIIS

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Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute

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Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system

Cited by 188 publications

References 41 publications

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy

Prediction Model of User Physical Activity using Data Characteristics-based Long Short-term Memory Recurrent Neural Networks

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