This review paper focuses on the use of ensemble neural networks (ENN) in the
development of storm surge flood models. Storm surges are a major concern in coastal …

Time series forecasting is important because it can often provide the foundation for decision
making in a large variety of fields. Statistical approaches have been extensively adopted for …

Time series prediction has been widely used in a variety of applications in science,
engineering, finance, etc. There are two different modeling options for constructing …

Background: Short Term Load Forecasting (STLF) can predict load from several minutes to
week plays a vital role to address challenges such as optimal generation, economic …

The aims of this paper were to provide a comprehensive review of classification techniques
and their alternative approaches in data mining. Classification is a data mining technique …

Gramophone records were the main recording medium for more than seven decades and
regained widespread popularity over the past several years. Being an analog storage …

Inspired by a multi-granularity and fractal theory, this work mainly focuses on how to
conceive a training and test dataset at different levels under a small dataset in a complex …

For improved drought planning and response, there is an increasing need for highly
predictive and stable drought prediction models. This paper presents the performance of …

In recent years, many researchers have been using neural network (NN) model as an
instrument of their research. The motivation behind the application of NN is that it can be …

The paper tackles with local models (LM) for periodical time series (TS) prediction. A novel
prediction method is introduced, which achieves high prediction accuracy by extracting …