This paper presents a methodology for using hydrodynamic modeling to estimate inundation areas and water depths during a hurricane event. The Environmental Fluid Dynamic Code (EFDC) is used in this research. EFDC is one of the most commonly applied models to Gulf of Mexico estuaries. The event with which the hydrodynamic model was tested was hurricane Ivan. This hurricane made landfall at the A...

[1] Hurricanes mix and cool the upper ocean, as shown here in observations and modeling of the Caribbean Sea and the Gulf of Mexico during the passage of hurricane Wilma. Curiously, the upper ocean around the Loop Current warmed prior to Wilma’s entrance into the Gulf. The major cause was increased volume and heat transports through the Yucatan Channel produced by storm-induced convergences in ...

Abstract: Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on vari...

Although artificial neural networks (ANN) promise superior performance in forecasting theory, they are not yet an established method in business practice. The vast degrees of freedom to parameterize ANNs have lead to countless heuristic approaches to simplify modeling, training, network selection and evaluation implemented with varying success. Consequently, a systematic evaluation is required ...

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted...

Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has bee...

The forecasting of financial variables is a problem which has been investigated ever since economies began to develop. Modern day information technology now provides at the fingertips of forecasters an enormous range of historical data, automated forecasts and news stories. In addition to assisting with traditional financial forecasting problems this has allowed financial time series to be inve...

Density forecasting in regression is gaining popularity as real world applications demand an estimate of the level of uncertainty in predictions. In this paper we describe the two goals of density forecasting sharpness and calibration. We review the evaluation methods available to a density forecaster to assess each of these goals and we introduce a new evaluation method that allows modelers to...

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implementation of an evaluation protocol. This Pilot Program enlisted three regional-scale air quality models, serving as pro...

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the are...