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This paper presents a novel multi-task learning framework for the accurate prediction of spatio-temporal data at multiple locations. The framework encodes the data as a third-order tensor and performs supervised tensor decomposition to identify the latent factors that capture the inherent spatiotemporal variabilities of the data and their relationship to the target variable of interest. The framework...
Precise prediction of the goods demand is an important element of the supply chain management because we can optimise the level of stock based on predicted demand. Demand of goods may vary influenced by numerous factors, including price elasticity and weather, which we focus in this paper. We analysed daily sales data of consumer goods collected from Point of Sale (POS) systems of Japanese retailers,...
This paper discusses a model that predicts trap counts of Culex tarsalis, a female mosquito that is responsible for West Nile Virus (WNV) using machine-learning algorithms. Culex mosquitoes are the main transmission vectors for WNV infections. In this research, a Partial Least Square Regression (PLSR) has been deployed to predict mosquito trap counts of Culex tarsalis using historical meteorological...
Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational test-bed for climate prediction markets. Traders adapt their beliefs about future temperatures based on the profits of other traders in their social network. We simulate two alternative climate futures, in which global temperatures...
In this paper, we try to solve site selection problem for building meteorological observation stations by recommending some locations. The functions of these stations are meteorological observation and prediction in regions without these. Thus in this paper two specific problems are solved. One is how to predict the meteorology in the regions without stations by using known meteorological data of...
Accurate electrical short term load forecasts play an important role for grid operation, power plant scheduling and power trading. The need for precise forecasts rises as energy markets are in a phase of transition due to severe changes in the energy system for European countries. This transition is mainly caused by rising shares of renewable energies, increasing energy efficiency and consumption...
Intelligent Transportation Systems are an important aspect of our life and are going to become ubiquitous in the near future. Traffic flow prediction is a key component of any Intelligent Transportation Systems. This report uses Artificial Neural Network based models to predict short term traffic flow. Two new input parameters; temperature and truck flow has been introduced into a multi input parameters...
A traffic incident is defined by an event which provokes a disruption on the normal (free) flow condition of any highway. Such incidents must be caused by a recurrent excessive demand or, in alternative, by a series of possible stochastic occurrences which may suddenly reduce the road capacity (e.g. car accidents, extreme weather changes). This paper proposes a novel binary supervised learning method...
In metropolitan areas, about 50% of traffic delays are caused by non-recurring traffic incidents. Hence, accurate prediction of the duration of such events is critical for traffic management authorities. In this paper, we study the predictability of the duration of traffic incidents by considering various external factors. As incident data is typically sparse, training a large number of models (for...
In the modern society, energy consumption such as gas and electricity is closely related to the weather condition because of the large share of weather-sensitive electrical appliances. Investigating how weather influences the energy consumption is of great significance for energy demand forecasting. This paper proposes an optimum regression approach for analyzing weather influence on the energy consumption...
This paper presents an approach for getting rainfall forecasting from the coupling the Weather Research and Forecasting model (WRF) with the Regional Ocean Model System (ROMS) model to be the uncertainty of hydrological model. The results from this coupling model are the average amount of rainfall forecasting in sub-basin areas. This operation is the automated image analysis and data entry process...
Frosts are one of the main risks faced by farmers during the winter and spring seasons. These events can cause significant damage to cultivations and crops. In Chile, these frost generates significant losses in the agricultural production sector, causing crop losses of an entire year and compromising the income of the following year, especially fruit and wine growers. In this work we developed a prediction...
As one of the most correlative impact factors of photovoltaic (PV) power output, the PV module temperature plays very important role in PV power forecasting, but often be confused with ambient temperature. In this paper, the research on impacts of ambient temperature and PV module temperature on power output of PV modules is analyzed to explore the differences and similarities of the two kinds of...
Information about climate changes is required at global, regional and basin levels for a variety of purposes, including the study of impact of the greenhouse gases. The analyses mentioned in this research relate to the observation of trends in the temperatures of the Indian states. The research begins with the exposition of the ongoing analysis methodologies prevalent in exploratory analysis and prediction...
Times series forecasting issue can be found in several subject areas as finance and business (e.g. foreign exchange rates, data for prices), industry (energy load and demand), climate and meteorology (e.g. sea surface temperature and El Nio phenomenon), health (e.g. prognosis from medical data) and many others. This paper is focused in univariate time series (x1, x2, …, xt), so unknown future values...
For present distribution transformer failure rate prediction model, load and weather forecast information in the future have been not considered. In this paper, distribution transformer failure rate prediction model with parameters is proposed. Firstly, the characteristics of several common failure rate models are analyzed. The failure rate model combined the health index and operating time is established...
The primary goal of the model proposed in this paper is to predict airline delays caused by inclement weather conditions using data mining and supervised machine learning algorithms. US domestic flight data and the weather data from 2005 to 2015 were extracted and used to train the model. To overcome the effects of imbalanced training data, sampling techniques are applied. Decision trees, random forest,...
With the increasing penetration of solar photovoltaic (PV) generation in the power system, the reliability of the distribution system and efficiency of PV systems have garnered increasing attention in recent years. Forecasting the PV output is one way to decrease the uncertainty of such power systems. In this study, we present a K-Nearest Neighbors algorithm based forecasting model, which can provide...
Atmospheric pollution is getting more and more attention because of the serious status. Finding the pollutant source is one of the key steps to solve this problem. This paper introduces a new method to trace the pollution diffusion path according to the relevance of different areas where they were polluted (the relatively small areas regard as points or lattices in this paper). We present the single...
The Martian atmosphere has Carbon Dioxide (CO2) as its main gas comprising of 95.4%. The second main gas is Nitrogen (N2) with 2.7%. All the other gases are in trace amounts, that is they are less than 1% in the atmosphere[6]. Even then these trace gases have a large contribution in the chemical reactions taking place in the atmosphere. These tracer gases are O2, Ozone (O3), Sulfur Dioxide (SO2),...
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