Feature Extraction Via Multiresolution MODWT Analysis in a Rainfall Forecast System

During 30 years, expert meteorologists have been sampling meteorological measurements directly related to the rainfall event, in order to improve the current forecast procedures. This study performs the Feature Extraction and Feature Selection processes to extract the relevant information in the rainfall event. The Feature Extraction has been performed with a Multiresolution Analysis applying the Maxima OverlapWavelet Transform. The selection of the wavelet decomposition, was obtained applying a Sequential Feature Selection algorithm based on General Regression Neural Networks. In this paper, it is also presented a novel architecture to perform short and medium term weather forecasts based on Neural Networks and time series estimation filters. The preliminary results obtained, present this architecture as a feasible alternative to the current forecast procedures performed by super computer simulation centers

Local Rainfall Forecast System based on Time Series Analysis and Neural Networks

Rainfall is one of the most important events in daily life of human beings. During several decades, scientists have been trying to characterize the weather, current forecasts are based on high complex dynamic models. In this paper is presented a local rainfall forecast system based on Time Series analysis and Neural Networks. This model tries to complement the currently state of the art ensembles, from a locally historical perspective, where the model definition is not so dependent from the exact values of the initial conditions. After several year taking data, expert meteorologists proposed this approximation to characterize the local weather behavior, that is being automated by this system in different stages. However the whole system is introduced, it is focused on the different rainfall events situation classification as well as the time series analysis and forecas

FMECA application to Rainfall Hazard prevention in olive trees growings

The FMECA (Failure Mode Effects and Criticality Analysis) is a broadly extended System Safety tool applied in industries as Aerospace in order to prevent hazards. This methodology studies the different failure modes of a system and try to mitigate them in a systematic procedure. In this paper this tool is applied in order to mitigate economical impact hazards derived from Rainfalls to olive trees growing in Granada (Spain), understanding hazard from the System Safety perspective (Any real or potential condition that can cause injury, illness, or death to personnel; damage to or loss of a system, equipment or property; or damage to the environment). The work includes a brief introduction to the System Safety and FMECA methodologies, applying then these concepts to analyze the Olive trees as a system and identify the hazards during the different stages of the whole life cycle plant productio

Combining multiscale filtering and neural networks for local rainfall forecast

Rainfall is one of the most important events of human life and society. Some rainfall phenomena like floods or hailstone are a threat to agriculture, business and even life. Predicting the weather has emerged as one of the most important areas of scientific endeavour. Nowadays, there is a big effort and great developments in long and mid-term rainfall forecasts, where qualitative improvements have been obtained both in forecasts and verification. This work proposes a diverse local rainfall forecasting system, using a long term local measurements registry. The forecast is performed estimating pressure time series and processing them with multispectral wavelet analysis and Neural Networks. The aim of the study is to provide complementary criteria based on the observed pressure wave pattern repetition. This method was proposed by expert meteorologists after observing these events during 40 years

RIE : revista de investigación educativa

Resumen de la revistaEste trabajo es una aplicación de la metodología DEA (Análisis Envolvente de Datos) al estudio de la eficiencia de Centros de Educación Secundaria en Murcia. Se trata de una técnica de optimización no-paramétrica que realiza sus evaluaciones e inferencias directamente de los datos observados. Como W.W. Cooper reconoce (1999), ciertos sectores como Sanidad y sobre todo Educación que dio origen al DEA se han resistido al resto de métodos existentes. En el artículo se calculan los ratios de eficiencia relativa de los diecisiete centros de Enseñanza Secundaria del Municipio de Murcia que en el curso 98-99 presentaron alumnos a las pruebas de Acceso a la Universidad, detectando las causas de ineficiencia e indicando cómo deben variar sus recursos y sus productos para convertirse en totalmente eficientes. Se realizan finalmente análisis global y de sensibilidad de los resultados.CataluñaES