From 0fb4833a6e020f3af82fb1c1d563813f3eb6173a Mon Sep 17 00:00:00 2001 From: Marco Vela <36453977+MarcoVela@users.noreply.github.com> Date: Tue, 7 Dec 2021 17:41:49 -0500 Subject: [PATCH] Tesis pregrado (#2) * avance marco teorico * marco institucional, legal y normativo * segundo entregable entrega del marco teorico y estado del arte * limpio removida la pagina de agradecimientos, dedicatoria y siglas --- .../0_0_PREAMBULO/Preambulo.tex | 1 + papers/tesis-pregrado/1_4_RESUMEN/Resumen.tex | 3 +- .../tesis-pregrado/1_7_SIMBOLOS/Simbolos.tex | 2 +- .../Secciones/1_Descripci\303\263n.tex" | 30 ++- .../Secciones/2_Formulaci\303\263n.tex" | 5 +- .../2_CAPITULO1/Secciones/3_Objetivos.tex | 17 +- .../Secciones/4_Justificaci\303\263n.tex" | 25 +- .../2_CAPITULO1/Secciones/5_Alcances.tex | 19 +- .../tesis-pregrado/2_CAPITULO2/Capitulo2.tex | 20 +- .../2_CAPITULO2/Secciones/1_Conceptual.tex | 17 ++ .../2_CAPITULO2/Secciones/2_Tecnologico.tex | 56 +++- .../2_CAPITULO2/Secciones/3_Institucional.tex | 50 +++- .../2_CAPITULO2/Secciones/4_Legal.tex | 38 ++- .../tesis-pregrado/2_CAPITULO3/Capitulo3.tex | 8 +- .../2_CAPITULO3/Secciones/1_Taxonomia.tex | 154 +++++++++++ .../Secciones/1_Taxonom\303\255a.tex" | 3 - .../2_CAPITULO3/Secciones/2_Benchmarking.tex | 108 ++++++++ .../Secciones/2_Revisi\303\263n.tex" | 8 - .../2_CAPITULO3/Secciones/3_Benchmarking.tex | 9 - .../3_3_BIBLIOGRAFIA/library.bib | 240 ++++++++++++++++++ .../E_IMAGENES/2_MarcoTeorico/PM02.png | Bin 0 -> 81922 bytes .../E_IMAGENES/2_MarcoTeorico/mapro.png | Bin 0 -> 373217 bytes .../E_IMAGENES/3_EstadoArte/chang_1.jpeg | Bin 0 -> 64017 bytes .../E_IMAGENES/3_EstadoArte/cintineo_1.jpg | Bin 0 -> 50367 bytes .../E_IMAGENES/3_EstadoArte/convlstm.png | Bin 0 -> 34395 bytes .../E_IMAGENES/3_EstadoArte/dgmr_1.jpg | Bin 0 -> 26610 bytes .../E_IMAGENES/3_EstadoArte/metnet.png | Bin 0 -> 159922 bytes .../E_IMAGENES/3_EstadoArte/taxonomia.png | Bin 0 -> 30737 bytes .../E_IMAGENES/3_EstadoArte/trajgru.png | Bin 0 -> 13169 bytes papers/tesis-pregrado/main.tex | 6 +- 30 files changed, 744 insertions(+), 75 deletions(-) create mode 100755 papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonomia.tex delete mode 100755 "papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonom\303\255a.tex" create mode 100755 papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Benchmarking.tex delete mode 100755 "papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Revisi\303\263n.tex" delete mode 100755 papers/tesis-pregrado/2_CAPITULO3/Secciones/3_Benchmarking.tex create mode 100755 papers/tesis-pregrado/E_IMAGENES/2_MarcoTeorico/PM02.png create mode 100755 papers/tesis-pregrado/E_IMAGENES/2_MarcoTeorico/mapro.png create mode 100755 papers/tesis-pregrado/E_IMAGENES/3_EstadoArte/chang_1.jpeg create mode 100755 papers/tesis-pregrado/E_IMAGENES/3_EstadoArte/cintineo_1.jpg create mode 100755 papers/tesis-pregrado/E_IMAGENES/3_EstadoArte/convlstm.png create mode 100755 papers/tesis-pregrado/E_IMAGENES/3_EstadoArte/dgmr_1.jpg create mode 100755 papers/tesis-pregrado/E_IMAGENES/3_EstadoArte/metnet.png create mode 100755 papers/tesis-pregrado/E_IMAGENES/3_EstadoArte/taxonomia.png create mode 100755 papers/tesis-pregrado/E_IMAGENES/3_EstadoArte/trajgru.png diff --git a/papers/tesis-pregrado/0_0_PREAMBULO/Preambulo.tex b/papers/tesis-pregrado/0_0_PREAMBULO/Preambulo.tex index 87640a1..c7e7850 100755 --- a/papers/tesis-pregrado/0_0_PREAMBULO/Preambulo.tex +++ b/papers/tesis-pregrado/0_0_PREAMBULO/Preambulo.tex @@ -3,6 +3,7 @@ \setlength{\bibsep}{5pt plus 0.3ex} %Espaciamiento en la bibliografía \setcounter{secnumdepth}{3} % Numera los subsubsecciones \usepackage[OT1]{fontenc} +\usepackage{float} \usepackage[spanish, es-tabla, es-ucroman]{babel} %% reemplaza "cuadro" por "tabla" \decimalpoint %Cambia coma por punto \usepackage{mathptmx} diff --git a/papers/tesis-pregrado/1_4_RESUMEN/Resumen.tex b/papers/tesis-pregrado/1_4_RESUMEN/Resumen.tex index 85f67da..2d137aa 100755 --- a/papers/tesis-pregrado/1_4_RESUMEN/Resumen.tex +++ b/papers/tesis-pregrado/1_4_RESUMEN/Resumen.tex @@ -2,5 +2,6 @@ \chapter*{\centerline {RESUMEN}} \markboth{RESUMEN}{} -\lipsum[5] +El objetivo del presente trabajo consiste en el desarrollo de un sistema capaz +de pronósticar la evolución de tormentas a corto plazo en el territorio peruano. diff --git a/papers/tesis-pregrado/1_7_SIMBOLOS/Simbolos.tex b/papers/tesis-pregrado/1_7_SIMBOLOS/Simbolos.tex index 388d377..bfd6175 100755 --- a/papers/tesis-pregrado/1_7_SIMBOLOS/Simbolos.tex +++ b/papers/tesis-pregrado/1_7_SIMBOLOS/Simbolos.tex @@ -2,5 +2,5 @@ \chapter*{\centerline {LISTA DE SIGLAS Y ACRÓNIMOS}} \markboth{LISTA DE SIGLAS Y ACRÓNIMOS}{} %--- - \input{1_7_SIMBOLOS/Secciones/2_Siglas.tex} + diff --git "a/papers/tesis-pregrado/2_CAPITULO1/Secciones/1_Descripci\303\263n.tex" "b/papers/tesis-pregrado/2_CAPITULO1/Secciones/1_Descripci\303\263n.tex" index 1187a4e..e88d434 100755 --- "a/papers/tesis-pregrado/2_CAPITULO1/Secciones/1_Descripci\303\263n.tex" +++ "b/papers/tesis-pregrado/2_CAPITULO1/Secciones/1_Descripci\303\263n.tex" @@ -1,3 +1,31 @@ \section{Descripción de la situación problema} -\lipsum[5] \ No newline at end of file +\subsection{Fatalidades por tormentas eléctricas} + +Según datos del SINADEF, desde el año 2017 hasta el 2021 se han registrado 277 +fallecimientos a causa de rayos. + +\begin{table}[h] + \centering + \label{tab:fallecimientos_rayo} + \caption{Fallecimientos a causa de rayos. Obtenido de SINADEF el 29/11/2021} + \begin{tabular}{cc} + $Año$ & $Fallecimientos$\\ + \hline + $2017$ & $54$\\ + $2018$ & $61$\\ + $2019$ & $56$\\ + $2020$ & $56$\\ + $2021$ & $50$\\ + \end{tabular} +\end{table} + + +\subsection{Perdidas económicas por tormentas eléctricas} + +De acuerdo a la nota técnica del SENAMHI sobre Lineamientos para el diseño de +sistemas integrados de vigilancia y pronóstico hidrometeorológico con fines de +alerta temprana, los la destrucción de infraestructura ocasionada por fenómenos +climáticos como El Niño Costero representa más de US\$ 3100 millones de dólares +americanos. + diff --git "a/papers/tesis-pregrado/2_CAPITULO1/Secciones/2_Formulaci\303\263n.tex" "b/papers/tesis-pregrado/2_CAPITULO1/Secciones/2_Formulaci\303\263n.tex" index d21478c..7e39ccc 100755 --- "a/papers/tesis-pregrado/2_CAPITULO1/Secciones/2_Formulaci\303\263n.tex" +++ "b/papers/tesis-pregrado/2_CAPITULO1/Secciones/2_Formulaci\303\263n.tex" @@ -1,7 +1,6 @@ \section{Formulación del problema} -\lipsum[8] - - +La necesidad de pronósticos acertados de tormentas eléctricas a corto plazo debe +ser cubierta para poder evitar perdidas humanas y económicas. diff --git a/papers/tesis-pregrado/2_CAPITULO1/Secciones/3_Objetivos.tex b/papers/tesis-pregrado/2_CAPITULO1/Secciones/3_Objetivos.tex index d72a79c..7ac46c5 100755 --- a/papers/tesis-pregrado/2_CAPITULO1/Secciones/3_Objetivos.tex +++ b/papers/tesis-pregrado/2_CAPITULO1/Secciones/3_Objetivos.tex @@ -1,15 +1,22 @@ \section{Objetivos} \subsection{Objetivo General} -\lipsum[2] - +Desarrollar un sistema capaz de realizar pronósticos acertados a corto plazo de +manera automática de tormentas eléctricas en el territorio peruano. + \subsection{Objetivos Específicos} \begin{itemize} -\item Objetivo específico 1. -\item Objetivo específico 2. +\item Definir el tipo de modelo de aprendizaje profundo y métricas de evaluación +apropiados. + +\item Preparar el conjunto de datos sobre el que se realizará el aprendizaje. + +\item Seleccionar la combinación de parámetros apropiada utilizando las métricas +de evaluación definidas. -\item Objetivo específico 3. +\item Desplegar e integrar el sistema de pronóstico automatizado para monitorear +el desplazamiento y evolución de tormentas en el territorio peruano. \end{itemize} diff --git "a/papers/tesis-pregrado/2_CAPITULO1/Secciones/4_Justificaci\303\263n.tex" "b/papers/tesis-pregrado/2_CAPITULO1/Secciones/4_Justificaci\303\263n.tex" index e715067..9bacf85 100755 --- "a/papers/tesis-pregrado/2_CAPITULO1/Secciones/4_Justificaci\303\263n.tex" +++ "b/papers/tesis-pregrado/2_CAPITULO1/Secciones/4_Justificaci\303\263n.tex" @@ -1,18 +1,25 @@ \section{Justificación} \subsection{Justificación práctica} -\lipsum[1] +A nivel mundial, tener conocimiento del tiempo, el clima e información sobre el +agua está aumentando, debido a la necesidad de los gobiernos de minimizar las +crecientes pérdidas económicas y ayudar a los países a adaptarse al cambio +climático. Estos factores afectan a las sociedades y economías a través de +eventos extremos, tales como inundaciones, heladas, friajes, sequías +prolongadas, y eventos climáticos de alto impacto que afectan la demanda de +electricidad y la capacidad de producción, fechas de siembra y cosecha, gestión +de la construcción, redes de transporte e inventarios, y salud humana entre los +temas más importantes. \subsection{Justificación académica} -\begin{itemize} -\item Hipótesis Específica 1. - -\item Hipótesis Específica 2. - -\item Hipótesis Específica 3. - -\end{itemize} +El estudio de secuencias espaciotemporales con métodos de aprendizaje profundo +está aún en sus primeras etapas en contraste con los tradicionales métodos +numéricos. Estos métodos numéricos realizan simulaciones físicas para estimar +parámetros climáticos, estas simulaciones requieren un gran poder computacional. +A diferencia de los métodos numéricos, el uso de modelos de aprendizaje +profundo tiene menores requerimientos computacionales. Los modelos de +aprendizaje profundo se benefician de la gran cantidad de datos disponibles. diff --git a/papers/tesis-pregrado/2_CAPITULO1/Secciones/5_Alcances.tex b/papers/tesis-pregrado/2_CAPITULO1/Secciones/5_Alcances.tex index 95cdadf..cb5e3b2 100755 --- a/papers/tesis-pregrado/2_CAPITULO1/Secciones/5_Alcances.tex +++ b/papers/tesis-pregrado/2_CAPITULO1/Secciones/5_Alcances.tex @@ -1,20 +1,9 @@ \section{Alcances} -La metodología seguida para el desarrollo de la presente tesis es la siguiente: - -\begin{itemize} -\item Paso 1. - -\item Paso 2. - -\item Paso 3. - -\item Paso 4. - - -\end{itemize} - - +Se va a desarrollar un sistema que monitoree de manera constante la presencia +de rayos en el territorio peruano y pueda pronósticar su desplazamiento y +evolución soportado por un modelo de aprendizaje profundo. +El modelo en cuestión pronosticará la densidad de descargas (FED). diff --git a/papers/tesis-pregrado/2_CAPITULO2/Capitulo2.tex b/papers/tesis-pregrado/2_CAPITULO2/Capitulo2.tex index 71944fb..ad9c755 100755 --- a/papers/tesis-pregrado/2_CAPITULO2/Capitulo2.tex +++ b/papers/tesis-pregrado/2_CAPITULO2/Capitulo2.tex @@ -1,15 +1,19 @@ \chapter{MARCO TEÓRICO} \markboth{CAPÍTULO \thechapter: MARCO TEÓRICO}{} -\lipsum[23] +En este capítulo se hará una revisión de conceptos relacionados al área de +estudio. Se revisarán también conceptos relacionados a las tecnologías empleadas +para el desarrollo del sistema. Se revisarán conceptos relacionados a las +instituciones involucradas en el estudio. Finalmente se revisarán conceptos +relacionados al ambito legal del uso de modelos de aprendizaje profundo. - \input{2_CAPITULO2/Secciones/1_Conceptual.tex} +\input{2_CAPITULO2/Secciones/1_Conceptual.tex} + +\input{2_CAPITULO2/Secciones/2_Tecnologico.tex} + +\input{2_CAPITULO2/Secciones/3_Institucional.tex} + +\input{2_CAPITULO2/Secciones/4_Legal.tex} - \input{2_CAPITULO2/Secciones/2_Tecnologico.tex} - - \input{2_CAPITULO2/Secciones/3_Institucional.tex} - - \input{2_CAPITULO2/Secciones/4_Legal.tex} - diff --git a/papers/tesis-pregrado/2_CAPITULO2/Secciones/1_Conceptual.tex b/papers/tesis-pregrado/2_CAPITULO2/Secciones/1_Conceptual.tex index 93af3ec..cf5235c 100755 --- a/papers/tesis-pregrado/2_CAPITULO2/Secciones/1_Conceptual.tex +++ b/papers/tesis-pregrado/2_CAPITULO2/Secciones/1_Conceptual.tex @@ -1 +1,18 @@ \section{Marco Conceptual} + +\subsection{Nowcasting} +El pronóstico a muy corto plazo, conocido como nowcasting fue definido por la +\cite{wmo2017} como el pronóstico con detalles locales, por cualquier método, +durante un período que va desde el presente hasta 6 horas, incluida una +descripción detallada del tiempo presente. + + +\subsection{Geostationary Lightning Mapper} +\cite{GOODMAN201334} definió a el Mapeador de Rayos Geoestacionario +(Geostationary Lightning Mapper, GLM) como un instrumento a bordo del Satélite +Geoestacionario Operacional Ambiental (Geostationary Operational Environmental +Satellite, GOES) que mapea actividad de rayos continuamente durante el día y +noche sobre las Américas y regiones oceánicas adyacentes. Conceptualmente, el +GLM es un detector de eventos de alta velocidad que opera en el espectro +infrarojo cercano. + diff --git a/papers/tesis-pregrado/2_CAPITULO2/Secciones/2_Tecnologico.tex b/papers/tesis-pregrado/2_CAPITULO2/Secciones/2_Tecnologico.tex index b5332a7..9e67b01 100755 --- a/papers/tesis-pregrado/2_CAPITULO2/Secciones/2_Tecnologico.tex +++ b/papers/tesis-pregrado/2_CAPITULO2/Secciones/2_Tecnologico.tex @@ -1 +1,55 @@ -\section{Marco Tecnologico} \ No newline at end of file +\section{Marco Tecnológico} + +\subsection{Aprendizaje automático} + +Según \cite{mitchell1997machine}, el aprendizaje automático (Machine Learning) +es el estudio de algoritmos que son capaces de mejorar su desempeño en tareas +específicas por medio del uso de datos. + +\subsection{Redes Neuronales Artificiales} + +Las redes neuronales artificiales son un modelo compuesto por múltiples capas +interconectadas de manera similar a las conexiones de neuronas en el cerebro +humano. Las diferentes arquitecturas de redes neuronales les dan el potencial +de ser aproximadores universales de funciones \cite{braspenning1995artificial}. +% Quizá incluir imagenes del simil entre redes neuronales y el cerebro + +\subsection{Aprendizaje profundo} + +Las técnicas de aprendizaje automático adolecen de un problema fundamental. +Requieren de dominio considerable del área de aplicación para extraer +características de los datos y poder transformarlos en una representación +adecuada para que el modelo pueda reconocer patrones. \cite{LeCun2015} definen +los métodos de aprendizaje profundo (Deep Learning) como métodos que permiten a +las máquinas descubrir representaciones significativas en conjuntos de datos +sin procesar. +% Incluir imagenes de un perceptron multicapas + + +\subsection{Redes Neuronales Convolucionales} + +Las redes neuronales convolucionales (Convolutional Neural Networks, CNN) son +una clase de red neuronal artificial inspiradas por procesos biológicos en los +que las neuronas responden a estímulos en una región restringida del campo +visual, denominado campo receptivo (Fukushima, \citeyear{Fukushima1980}). +\cite{Ciresan2011FlexibleHP} popularizaron su uso al demostrar que la +aceleración por hardware podía reducir el tiempo de procesamiento en un factor +de 60. En ese mismo año, usaron dicha implementación para ganar un concurso de +reconocimiento de imagenes, despertando más interés en la comunidad de visión +por computador. +% Incluir imagenes de la operación de convolución +% Hablar sobre los parámetros + + +\subsection{Redes Neuronales Recurrentes} + +Las redes neuronales recurrentes (Recurrent Neural Networks, RNN) son una clase +de red neuronal artificial que permite modelar comportamiento secuencial a lo +largo de una dimensión (usualmente temporal). Las RNN utilizan un estado interno +denominado memoria para poder procesar una secuencia de longitud variable de +entradas \cite{Abiodun2018}. +% Incluir imagenes sobre el tratamiento de secuencias +% Hablar sobre distintos tipos de RNN + + + diff --git a/papers/tesis-pregrado/2_CAPITULO2/Secciones/3_Institucional.tex b/papers/tesis-pregrado/2_CAPITULO2/Secciones/3_Institucional.tex index fcce796..6ea2080 100755 --- a/papers/tesis-pregrado/2_CAPITULO2/Secciones/3_Institucional.tex +++ b/papers/tesis-pregrado/2_CAPITULO2/Secciones/3_Institucional.tex @@ -1 +1,49 @@ -\section{Marco Institucional} \ No newline at end of file +\section{Marco Institucional} + +El presente trabajo se desarrolla en conjunto con el Servicio Nacional de +Meteorología e Hidrología del Perú (SENAMHI) para apoyar uno de sus procesos +misionales. + +\subsection{Servicio Nacional de Meteorología e Hidrología del Perú} + +El SENAMHI es un organismo público adscrito al Ministerio del Ambiente (MINAM). +Tiene como misión generar y proveer información y conocimiento meteorológico, +hidrológico y climático para la sociedad peruana de manera oportuna y +confiable, contribuyendo de esta manera a la reducción de los impactos +negativos producidos por los fenómenos naturales de origen hidrometeorológico. + +\begin{figure}[H] + \centering + \includegraphics[height=8cm]{E_IMAGENES/2_MarcoTeorico/mapro} + \caption{ + Mapa de Procesos del SENAMHI.\\ + Fuente: Manual de Procesos y Procedimientos 2020. + } + \label{fig:mapro} +\end{figure} + +Según \cite{senamhi2020rof} entre sus funciones se encuentra ``Organizar, normar +y promover un sistema de vigilancia del medio ambiente atmosférico del país, a +fin de prevenir los peligros de la contaminación ambiental''. Esta función se +materializa en el proceso misional 02 ``Gestión de Vigilancia y Pronóstico'' del +Mapa de Procesos que se observa en la figura \ref{fig:mapro}. + +\begin{figure}[H] + \centering + \includegraphics[height=8cm]{E_IMAGENES/2_MarcoTeorico/PM02} + \caption{ + Proceso misional 02: Gestión de vigilancia y pronóstico\\ + Fuente: Manual de Procesos y Procedimientos 2020. + } + \label{fig:pm02} +\end{figure} + +El proceso M.02.03, ``Gestión de Pronósticos Meteorológicos, Hidrológicos, +Agrometeorológicos, Climáticos y Ambiental Atmosféricos'', que se observa en la +figura \ref{fig:pm02} comprende entre sus procedimientos la elaboración de +pronósticos del tiempo y tiene como uno de sus productos el pronóstico a muy +corto plazo. + +De acuerdo a \cite{senamhi2021govdig}, es parte del Plan de Gobierno Digital del +SENAMHI la ampliación de funcionalidades del sistema de pronóstico inmediato +(nowcasting). diff --git a/papers/tesis-pregrado/2_CAPITULO2/Secciones/4_Legal.tex b/papers/tesis-pregrado/2_CAPITULO2/Secciones/4_Legal.tex index 1b25553..f446489 100755 --- a/papers/tesis-pregrado/2_CAPITULO2/Secciones/4_Legal.tex +++ b/papers/tesis-pregrado/2_CAPITULO2/Secciones/4_Legal.tex @@ -1 +1,37 @@ -\section{Marco Legal} \ No newline at end of file +\section{Marco Legal y Normativo} + +\subsection{Lineamientos para el diseño de sistemas integrados de vigilancia y +pronóstico hidrometeorológico con fines de alerta temprana} + +\cite{senamhi2021lineamientos} indica que los servicios y productos asociados a +la alerta temprana deben obedecer el principio de subsidiariedad, lo que +significa que los avisos de peligro a muy corto plazo deben poder ser generados +por las oficinas desconcentradas del SENAMHI.\newline +La continuidad del servicio requiere además un sistema robusto y resiliente, +siguiendo el principio de subsidiariedad, la sede central del SENAMHI apoyaría +a los organos desconcentrados en la generación o entrega de productos en caso +de que sus capacidades se vean sobrepasadas. + +\subsection{Recommendation of the Council on Artificial Intelligence} + +De acuerdo con \cite{oecd2019recom} uno de los principios para la +administración responsable de la inteligencia artificial es la +\textbf{Robustez, seguridad y protección}: +\begin{itemize} + \item Los sistemas de IA deben ser robustos y seguros durante todo su ciclo + de vida de tal manera que, en condiciones de uso normal, uso o maluso + previsible, u otras condiciones adversas, funcionen apropiadamente y no + planteen un riesgo de seguridad no razonable. + \item Para este fin, los actores involucrados deben asegurar la trazabilidad, + incluso en relación a los conjuntos de datos, procesos y decisiones hechas + durante el ciclo de vida del sistema, para habilitar el análisis de los + resultados del sistema. + \item Los actores deberán, basado en sus roles, aplicar un enfoque + sistemático de gestión de riesgos a cada fase del ciclo de vida del sistema + para atender los riesgos relacionados a sistemas de IA, incluyendo seguridad + digital y sesgo. +\end{itemize} + + +% Lineamientos para el diseño de sistemas integrados de vigilancia y pronóstico hidrometeorológico con fines de alerta temprana +% ENIA Peru diff --git a/papers/tesis-pregrado/2_CAPITULO3/Capitulo3.tex b/papers/tesis-pregrado/2_CAPITULO3/Capitulo3.tex index eeacf58..e2440c0 100755 --- a/papers/tesis-pregrado/2_CAPITULO3/Capitulo3.tex +++ b/papers/tesis-pregrado/2_CAPITULO3/Capitulo3.tex @@ -1,13 +1,9 @@ \chapter{ESTADO DEL ARTE} \markboth{CAPÍTULO \thechapter: ESTADO DEL ARTE}{} -\lipsum [6] +\input{2_CAPITULO3/Secciones/1_Taxonomia.tex} - \input{2_CAPITULO3/Secciones/1_Taxonomía.tex} - - \input{2_CAPITULO3/Secciones/2_Revisión.tex} - - \input{2_CAPITULO3/Secciones/3_Benchmarking.tex} +\input{2_CAPITULO3/Secciones/2_Benchmarking.tex} diff --git a/papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonomia.tex b/papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonomia.tex new file mode 100755 index 0000000..a35645d --- /dev/null +++ b/papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonomia.tex @@ -0,0 +1,154 @@ +\section{Taxonomía} + +Se ha desarrollado una cantidad significativa de trabajo en el área de +pronóstico a muy corto plazo de secuencias espaciotemporales. Dado que +el pronóstico de tormentas eléctricas está intimamente relacionado con el +pronóstico de secuencias espaciotemporales este capítulo presentará una +taxonomía de enfoques de pronóstico de secuencias espaciotemporales a muy +corto plazo.\newline +La taxonomía presentada en la figura \ref{fig:taxonomia} en forma de árbol, +dividida en dos ramas principales para las categorías de enfoques utilizados. +Cada una de estas ramas es dividida en subcategorías de enfoques utilizados. +Cada enfoque utilizado es subidividido en métodos utilizados. + +\begin{figure}[H] + \centering + \includegraphics[width=14cm]{./E_IMAGENES/3_EstadoArte/taxonomia} + \caption{ + Una taxónomía de los enfoques aplicados a la predicción de secuencias + espaciotemporales a muy corto plazo. Categorías extraidas de + \cite{DBLP:journals/corr/abs-1808-06865}. + } + \label{fig:taxonomia} +\end{figure} + +El foco del presente trabajo está en el uso de técnicas de deep learning, por +lo que no se realizaron mayores subdivisiones en los métodos clásicos. + +\subsection{Enfoques Clásicos} +Dado que una secuencia espaciotemporal puede ser vista como una secuencia +multivariada, los métodos diseñados para el pronóstico de series de tiempo +multivariadas son aplicables a secuencias espaciotemporales. + +\subsubsection{State Space Models} +Los Modelos de Espacio de Estados (State Space Models, SSM) analiza a las +secuencias desde un punto de vista generativo, donde cada observación $X_t$ se +asume generada por un estado oculto $H_t$ y esos estados ocultos forman un +proceso de Markov. Ejemplos notables de este tipo de modelos son los +Autoregressive Integrated Moving Average Model (ARIMA). + +\subsubsection{Feature-based Models} +Los modelos basados en características resuelve el pronóstico de secuencias +espaciotemporales al entrenar un modelo de regresión basado en características +diseñadas por humanos. + +\subsection{Enfoques Deep Learning} +Los enfoques de deep learning propuestos para el modelamiento de secuencias +espaciotemporales aprovechan las capacidades de generalización de las redes +neuronales profundas. + +\subsubsection{Deep Temporal Generative Models} +Los Modelos Generativos Profundos son modelos estadísticos que aprenden +distribuciones de probabilidad de los datos y permiten facil generación de +muestras de sus distribuciones aprendidas. + +\begin{itemize} + \item \textbf{DGM}: \cite{Ravuri2021} propuso el uso de Redes Generativas + Adversariales (Generative Adversarial Network, GAN) para modelar datos de + precipitación de radares meteorológicos en Reino Unido. El modelo propuesto + busca resolver el problema de pronósticos difuminados aprovechando la + capacidad de las GAN para generar pronósticos feasibles a cualquier plazo a + futuro. + \begin{figure}[H] + \centering + \includegraphics[width=8cm]{E_IMAGENES/3_EstadoArte/dgmr_1} + \caption{ + Modelo de pronóstico de lluvia a muy corto plazo utilizando Redes + Generativas Adversariales. Fuente: \cite{Ravuri2021} + } + \label{fig:dgmr_modelo} + \end{figure} +\end{itemize} + +\subsubsection{FNN \& RNN} +\begin{itemize} + \item \textbf{2D \& 3D CNN}: \cite{Cintineo2020} propuso el uso de Redes + Neuronales Convolucionales para pronósticar la probabilidad de convección + intensa. Su trabajo fue complementado con la explicabilidad de los + pronósticos, descubriendo los factores más significativos en el resultado del + modelo. + \begin{figure}[H] + \centering + \includegraphics[width=10cm]{E_IMAGENES/3_EstadoArte/cintineo_1} + \caption{ + Arquitectura de redes neuronales convolucionales para el pronóstico de + convección intensa. Fuente: \cite{Cintineo2020}. + } + \label{fig:cintineo} + \end{figure} + \cite{Chang2018} propuso el uso de datos topográficos para mejorar el + desempeño un modelo de pronóstico de precipitación, obteniendo mejor + desempeño que el modelo inicial, sin embargo, la proporción de falsa alarma + también se incrementó. Este artículo propone la incorporación de información + topográfica usando un bloque de convolución y max pooling como se observa en + la figura \ref{fig:chang} + \begin{figure}[H] + \centering + \includegraphics[width=8cm]{E_IMAGENES/3_EstadoArte/chang_1} + \caption{ + Arquitectura de redes neuronales convolucionales que incorpora datos + topográficos. Fuente: \cite{Chang2018}. + } + \label{fig:chang} + \end{figure} + + \item \textbf{ConvLSTM \& TrajGRU}: \cite{DBLP:journals/corr/ShiCWYWW15} + propuso una arquitectura de redes neuronales que combina la capacidad de + capturar correlación espacial de las CNN y la capacidad de capturar + correlación temporal de las LSTM, la denominó ConvLSTM. Las ConvLSTM utilizan + un estado oculto multidimensional para poder capturar información espacial y + actualizan sus estados por medio de la operación de convolución. + \begin{figure}[H] + \centering + \includegraphics[width=8cm]{E_IMAGENES/3_EstadoArte/convlstm} + \caption{ + Arquitectura de red neuronal convolucional LSTM para el pronóstico + inmediato de precipitación. Fuente: \cite{DBLP:journals/corr/ShiCWYWW15}. + } + \label{fig:convlstm} + \end{figure} + \cite{DBLP:journals/corr/ShiGL0YWW17} evaluó el desempeño de distintos + modelos de pronóstico inmediato para precipitación y propuso una nueva + arquitectura que resolvería el problema de la falta de sensibilidad a la + rotación de la arquitectura ConvLSTM, la denominó TrajGRU. + \begin{figure}[H] + \centering + \includegraphics[width=8cm]{E_IMAGENES/3_EstadoArte/trajgru} + \caption{ + Arquitectura de red neuronal Trajectory GRU para el pronóstico + inmediato de precipitación. Fuente: \cite{DBLP:journals/corr/ShiGL0YWW17}. + } + \label{fig:trajgru} + \end{figure} + + \item \textbf{Atention based}: \cite{DBLP:journals/corr/abs-2003-12140} + propuso una arquitectura que utilice el mecanismo de atención para poder + capturar correlaciones temporales a largo plazo, la denominó MetNet. Su + arquitectura presentada en la figura \ref{fig:metnet} entrega como salida + distribuciones de probabilidad de precipitiación con un desempeño que supera + a los modelos actualmente empleados. El modelo cuenta con 225M parámetros + entrenables, por lo que su entrenamiento y despliegue requiere de un enorme + poder computacional. + \begin{figure}[H] + \centering + \includegraphics[width=12cm]{E_IMAGENES/3_EstadoArte/metnet} + \caption{ + Arquitectura de red neuronal MetNet. Fuente: + \cite{DBLP:journals/corr/abs-2003-12140}. + } + \label{fig:metnet} + \end{figure} + +\end{itemize} + + diff --git "a/papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonom\303\255a.tex" "b/papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonom\303\255a.tex" deleted file mode 100755 index fc87de8..0000000 --- "a/papers/tesis-pregrado/2_CAPITULO3/Secciones/1_Taxonom\303\255a.tex" +++ /dev/null @@ -1,3 +0,0 @@ -\section{Taxonomía} - -\lipsum[3] diff --git a/papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Benchmarking.tex b/papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Benchmarking.tex new file mode 100755 index 0000000..63f96d9 --- /dev/null +++ b/papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Benchmarking.tex @@ -0,0 +1,108 @@ +\section{Benchmarking} + +Se realizará una comparación entre los métodos descritos en el estado del arte. +Se utilizarán criterios con pesos asignados siguiendo el Proceso Analítico +Jerarquico (Analytical Hierarchical Process, AHP). + +\subsection{Criterios} + +\begin{itemize} + \item \textbf{C1. Complejidad de entrenamiento:} Es el tiempo que + el modelo necesita para generalizar los datos, estimado en base a la + complejidad del modelo por usando el númeo de parámetros como indicador. + \item \textbf{C2. Desempeño a muy corto plazo:} Es el desempeño definido por + las métricas de evaluación pertinentes. + \item \textbf{C3. Explicabilidad:} Es la capacidad de reconocer los factores + que determinan el resultado del modelo. Es importante para poder obtener un + mayor conocimiento del fenómeno de estudio. + \item \textbf{C4. Replicabilidad:} Es la disponibilidad de recursos que + soporten el desarrollo de un tipo de método y su uso con los datos + disponibles. +\end{itemize} + +\subsection{Cálculo de pesos} +Se utilizó la escala de Saaty (ver tabla \ref{tab:escala_saaty}), para poder +determinar la importancia relativa de los criterios respecto a ellos mismos. + +\begin{table}[H] + \centering + \caption{Escala de Saaty} + \begin{tabular}{ll} + \textbf{Intensidad} & \textbf{Definición} \\ \hline + 1 & Igual importancia \\ + 3 & Moderada importancia \\ + 5 & Importancia fuerte \\ + 7 & Importancia muy fuerte \\ + 9 & Importancia extrema \\ + 2, 4, 6, 7 & Valores intermedios + \end{tabular} + \label{tab:escala_saaty} +\end{table} + + +\begin{table}[H] + \centering + \caption{Matriz de comparaciones pareadas.} + \begin{tabular}{l|cccc} + \hline + \textbf{} & \multicolumn{1}{l}{C1} & \multicolumn{1}{l}{C2} & \multicolumn{1}{l}{C3} & \multicolumn{1}{l}{C4} \\ \hline + C1 & 1 & 3 & 5 & 1/5 \\ + C2 & 1/3 & 1 & 3 & 1/3 \\ + C3 & 1/5 & 1/3 & 1 & 3 \\ + C4 & 5 & 3 & 1/3 & 1 \\ \hline + \textbf{Suma} & 98/15 = 6.53 & 22/3 = 7.33 & 28/3 = 9.33 & 68/15 = 4.53 \\ \hline + \end{tabular} + \label{tab:comp_pareadas} +\end{table} + +\begin{table}[H] + \centering + \caption{Cálculo de los pesos.} + \begin{tabular}{l|cccc|l} + \hline + \textbf{} & \multicolumn{1}{l}{C1} & \multicolumn{1}{l}{C2} & \multicolumn{1}{l}{C3} & \multicolumn{1}{l|}{C4} & \textbf{Peso} \\ \hline + C1 & 0.153 & 0.409 & 0.536 & 0.0442 & 0.2855 \\ + C2 & 0.0510 & 0.136 & 0.322 & 0.0736 & 0.1457 \\ + C3 & 0.0306 & 0.0455 & 0.107 & 0.662 & 0.2113 \\ + C4 & 0.766 & 0.409 & 0.0357 & 0.221 & 0.358 \\ \hline + \end{tabular} + \label{tab:comp_pareadas_norm} +\end{table} + +De la tabla \ref{tab:comp_pareadas_norm} obtenemos que el criterio 4 +(Replicabilidad) es el más importante, seguido de Complejidad de entrenamiento, +Explicabilidad y finalmente el desempeño a corto plazo. Esto se dió debido a +que el stakeholder valora más una solución funcional que pueda ir mejorando con +el tiempo a una solución compleja cuya implementación y explicabilidad sea +baja. + +\subsection{Evaluación} +Las alternativas a evaluar son: + +\begin{itemize} + \item \textbf{A1. DGM} + \item \textbf{A2. 2D \& 3D CNN} + \item \textbf{A3. ConvLSTM \& TrajGRU} + \item \textbf{A4. Atention Based} +\end{itemize} + +\begin{table}[H] + \centering + \caption{Evaluación de las alternativas.} + \begin{tabular}{l|ccccl} + \hline + \textbf{} & \multicolumn{1}{l}{C1} & \multicolumn{1}{l}{C2} & \multicolumn{1}{l}{C3} & \multicolumn{1}{l}{C4} & Total \\ \hline + A1 & 0.0833 & 0.324 & 0.0928 & 0.0657 & 0.1141 \\ + A2 & 0.333 & 0.221 & 0.3571 & 0.3947 & 0.3440 \\ + A3 & 0.416 & 0.0567 & 0.4285 & 0.4802 & 0.3894 \\ + A4 & 0.166 & 0.396 & 0.1214 & 0.05921 & 0.15193 \\ \hline + \textbf{Ponderación} & \multicolumn{1}{l}{0.2855} & \multicolumn{1}{l}{0.1457} & \multicolumn{1}{l}{0.2113} & \multicolumn{1}{l}{0.358} & \\ \hline + \end{tabular} + \label{tab:evaluacion} + +\end{table} + +\subsection{Decisión} + +De acuerdo a los puntajes obtenidos en la tabla \ref{tab:evaluacion}, se elige +la alternativa 3 (ConvLSTM \& TrajGRU). diff --git "a/papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Revisi\303\263n.tex" "b/papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Revisi\303\263n.tex" deleted file mode 100755 index 8fa3ad2..0000000 --- "a/papers/tesis-pregrado/2_CAPITULO3/Secciones/2_Revisi\303\263n.tex" +++ /dev/null @@ -1,8 +0,0 @@ -\section{Revisión de literatura} - - -\lipsum[5] - - - - diff --git a/papers/tesis-pregrado/2_CAPITULO3/Secciones/3_Benchmarking.tex b/papers/tesis-pregrado/2_CAPITULO3/Secciones/3_Benchmarking.tex deleted file mode 100755 index 5d85947..0000000 --- a/papers/tesis-pregrado/2_CAPITULO3/Secciones/3_Benchmarking.tex +++ /dev/null @@ -1,9 +0,0 @@ -\section{Benchmarking} - -\lipsum[10] - -\lipsum[11] - - - - diff --git a/papers/tesis-pregrado/3_3_BIBLIOGRAFIA/library.bib b/papers/tesis-pregrado/3_3_BIBLIOGRAFIA/library.bib index e69de29..93c6d36 100755 --- a/papers/tesis-pregrado/3_3_BIBLIOGRAFIA/library.bib +++ b/papers/tesis-pregrado/3_3_BIBLIOGRAFIA/library.bib @@ -0,0 +1,240 @@ +@article{Abiodun2018, + doi = {10.1016/j.heliyon.2018.e00938}, + url = {https://doi.org/10.1016/j.heliyon.2018.e00938}, + year = {2018}, + month = nov, + publisher = {Elsevier {BV}}, + volume = {4}, + number = {11}, + pages = {e00938}, + author = {Oludare Isaac Abiodun and Aman Jantan and Abiodun Esther Omolara and Kemi Victoria Dada and Nachaat AbdElatif Mohamed and Humaira Arshad}, + title = {State-of-the-art in artificial neural network applications: A survey}, + journal = {Heliyon} +} + +@book{braspenning1995artificial, + author = {Braspenning, P. J.}, + title = {Artificial neural networks : an introduction to ANN theory and practice}, + publisher = {Springer}, + year = {1995}, + address = {Berlin New York}, + isbn = {978-0387594880} +} + +@inproceedings{Ciresan2011FlexibleHP, + author = {Cire\c{s}an, Dan C. and Meier, Ueli and Masci, Jonathan and Gambardella, Luca M. and Schmidhuber, J\"{u}rgen}, + title = {Flexible, High Performance Convolutional Neural Networks for Image Classification}, + year = {2011}, + isbn = {9781577355144}, + publisher = {AAAI Press}, + abstract = {We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.}, + booktitle = {Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Two}, + pages = {1237–1242}, + numpages = {6}, + location = {Barcelona, Catalonia, Spain}, + series = {IJCAI'11} +} + +@article{Fukushima1980, + doi = {10.1007/bf00344251}, + url = {https://doi.org/10.1007/bf00344251}, + year = {1980}, + month = apr, + publisher = {Springer Science and Business Media {LLC}}, + volume = {36}, + number = {4}, + pages = {193--202}, + author = {Kunihiko Fukushima}, + title = {Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position}, + journal = {Biological Cybernetics} +} + +@article{GOODMAN201334, + title = {The GOES-R Geostationary Lightning Mapper (GLM)}, + journal = {Atmospheric Research}, + volume = {125-126}, + pages = {34-49}, + year = {2013}, + issn = {0169-8095}, + doi = {https://doi.org/10.1016/j.atmosres.2013.01.006}, + url = {https://www.sciencedirect.com/science/article/pii/S0169809513000434}, + author = {Steven J. Goodman and Richard J. Blakeslee and William J. Koshak and Douglas Mach and Jeffrey Bailey and Dennis Buechler and Larry Carey and Chris Schultz and Monte Bateman and Eugene McCaul and Geoffrey Stano}, + keywords = {Lightning, Thunderstorms, Satellite meteorology, Nowcasting}, + abstract = {The Geostationary Operational Environmental Satellite R-series (GOES-R) is the next block of four satellites to follow the existing GOES constellation currently operating over the Western Hemisphere. Advanced spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved cloud and moisture imagery with the 16-channel Advanced Baseline Imager (ABI). The GLM will map total lightning activity continuously day and night with near-uniform storm-scale spatial resolution of 8km with a product refresh rate of less than 20s over the Americas and adjacent oceanic regions in the western hemisphere. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low Earth orbit, and from ground-based lightning networks and intensive prelaunch field campaigns. The GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extend their combined climatology over the western hemisphere into the coming decades. Science and application development along with preoperational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and checkout of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.} +} + +@article{LeCun2015, + doi = {10.1038/nature14539}, + url = {https://doi.org/10.1038/nature14539}, + year = {2015}, + month = may, + publisher = {Springer Science and Business Media {LLC}}, + volume = {521}, + number = {7553}, + pages = {436--444}, + author = {Yann LeCun and Yoshua Bengio and Geoffrey Hinton}, + title = {Deep learning}, + journal = {Nature} +} + +@book{mitchell1997machine, + author = {Mitchell, Tom}, + title = {Machine Learning}, + publisher = {McGraw-Hill}, + year = {1997}, + address = {New York}, + isbn = {0-07-042807-7} +} + +@book{oecd2019recom, + title = {Recommendation of the Council on Artificial Intelligence}, + author = {{OECD}}, + year = 2019 +} + +@book{senamhi2020rof, + title = {Reglamento de Organización y Funciones del Servicio Nacional de Meteorología e Hidrología del Perú}, + author = {{SENAMHI}}, + year = 2020, + address = {Perú} +} + +@book{senamhi2021govdig, + title = {PLAN DE GOBIERNO DIGITAL 2021 – 2023}, + author = {{SENAMHI}}, + year = 2021, + address = {Perú} +} + +@book{senamhi2021lineamientos, + title = {Lineamientos para el diseño de sistemas integrados de vigilancia y pronóstico hidrometeorológico con fines de alerta temprana}, + author = {{SENAMHI}}, + year = 2021, + address = {Perú} +} + +@book{wmo2017, + title = {Guidelines for Nowcasting Techniques}, + author = {{World Meteorological Organization}}, + year = 2017, + publisher = {World Meteorological Organization }, + address = {Switzerland} +} + +@article{DBLP:journals/corr/abs-1808-06865, + author = {Xingjian Shi and + Dit{-}Yan Yeung}, + title = {Machine Learning for Spatiotemporal Sequence Forecasting: {A} Survey}, + journal = {CoRR}, + volume = {abs/1808.06865}, + year = {2018}, + url = {http://arxiv.org/abs/1808.06865}, + eprinttype = {arXiv}, + eprint = {1808.06865}, + timestamp = {Sun, 02 Sep 2018 15:01:56 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-1808-06865.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + + +@article{Ravuri2021, + doi = {10.1038/s41586-021-03854-z}, + url = {https://doi.org/10.1038/s41586-021-03854-z}, + year = {2021}, + month = sep, + publisher = {Springer Science and Business Media {LLC}}, + volume = {597}, + number = {7878}, + pages = {672--677}, + author = {Suman Ravuri and Karel Lenc and Matthew Willson and Dmitry Kangin and Remi Lam and Piotr Mirowski and Megan Fitzsimons and Maria Athanassiadou and Sheleem Kashem and Sam Madge and Rachel Prudden and Amol Mandhane and Aidan Clark and Andrew Brock and Karen Simonyan and Raia Hadsell and Niall Robinson and Ellen Clancy and Alberto Arribas and Shakir Mohamed}, + title = {Skilful precipitation nowcasting using deep generative models of radar}, + journal = {Nature} +} + +@article{Cintineo2020, + doi = {10.1175/waf-d-20-0028.1}, + url = {https://doi.org/10.1175/waf-d-20-0028.1}, + year = {2020}, + month = dec, + publisher = {American Meteorological Society}, + volume = {35}, + number = {6}, + pages = {2567--2588}, + author = {John L. Cintineo and Michael J. Pavolonis and Justin M. Sieglaff and Anthony Wimmers and Jason Brunner and Willard Bellon}, + title = {A Deep-Learning Model for Automated Detection of Intense Midlatitude Convection Using Geostationary Satellite Images}, + journal = {Weather and Forecasting} +} + +@inproceedings{Chang2018, + doi = {10.1109/ic3.2018.00018}, + url = {https://doi.org/10.1109/ic3.2018.00018}, + year = {2018}, + month = aug, + publisher = {{IEEE}}, + author = {Jui-Hung Chang and Ren-Hung Hwang and Yi-Jhong Gong and Kai-Hsiang Lin}, + title = {Applying Classification to Rainfall Nowcasting with Topographical Awareness}, + booktitle = {2018 1st International Cognitive Cities Conference ({IC}3)} +} + +@article{DBLP:journals/corr/ShiCWYWW15, + author = {Xingjian Shi and + Zhourong Chen and + Hao Wang and + Dit{-}Yan Yeung and + Wai{-}Kin Wong and + Wang{-}chun Woo}, + title = {Convolutional {LSTM} Network: {A} Machine Learning Approach for Precipitation + Nowcasting}, + journal = {CoRR}, + volume = {abs/1506.04214}, + year = {2015}, + url = {http://arxiv.org/abs/1506.04214}, + eprinttype = {arXiv}, + eprint = {1506.04214}, + timestamp = {Mon, 13 Aug 2018 16:48:00 +0200}, + biburl = {https://dblp.org/rec/journals/corr/ShiCWYWW15.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{DBLP:journals/corr/ShiGL0YWW17, + author = {Xingjian Shi and + Zhihan Gao and + Leonard Lausen and + Hao Wang and + Dit{-}Yan Yeung and + Wai{-}Kin Wong and + Wang{-}chun Woo}, + title = {Deep Learning for Precipitation Nowcasting: {A} Benchmark and {A} + New Model}, + journal = {CoRR}, + volume = {abs/1706.03458}, + year = {2017}, + url = {http://arxiv.org/abs/1706.03458}, + eprinttype = {arXiv}, + eprint = {1706.03458}, + timestamp = {Mon, 13 Aug 2018 16:46:25 +0200}, + biburl = {https://dblp.org/rec/journals/corr/ShiGL0YWW17.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + +@article{DBLP:journals/corr/abs-2003-12140, + author = {Casper Kaae S{\o}nderby and + Lasse Espeholt and + Jonathan Heek and + Mostafa Dehghani and + Avital Oliver and + Tim Salimans and + Shreya Agrawal and + Jason Hickey and + Nal Kalchbrenner}, + title = {MetNet: {A} Neural Weather Model for Precipitation Forecasting}, + journal = {CoRR}, + volume = {abs/2003.12140}, + year = {2020}, + url = {https://arxiv.org/abs/2003.12140}, + eprinttype = {arXiv}, + eprint = {2003.12140}, + timestamp = {Fri, 03 Apr 2020 14:04:20 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-2003-12140.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} \ No newline at end of file diff --git a/papers/tesis-pregrado/E_IMAGENES/2_MarcoTeorico/PM02.png b/papers/tesis-pregrado/E_IMAGENES/2_MarcoTeorico/PM02.png new file mode 100755 index 0000000000000000000000000000000000000000..f1ebe1708f9da1384b40165590b40e10b0300f16 GIT binary patch literal 81922 zcmb@u1yoes_XcbsAOa!?($XNM42^(v4J{yz#K6$qp&%k1QbS4&J#?1{0@5&aBRO=6 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\include{1_2_DEDICATORIA/Dedicatoria} + %\include{1_2_DEDICATORIA/Dedicatoria} - \include{1_3_AGRADECIMIENTOS/Agradecimientos} + %\include{1_3_AGRADECIMIENTOS/Agradecimientos} %Cambiar nombre y crear ÍNDICE @@ -46,7 +46,7 @@ %\cleardoublepage\phantomsection\addcontentsline{toc}{chapter}{\listfigurename} {\normalsize\listoffigures} - \include{1_7_SIMBOLOS/Simbolos} + %\include{1_7_SIMBOLOS/Simbolos} %Parte CENTRAL DE LA TESIS

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