Tópicos Sugeridos dentro de su búsqueda.
Tópicos Sugeridos dentro de su búsqueda.
1
artículo
The last decade has seen a major innovation within disaster risk management through the emergence of standardized forecast-based action and financing protocols. Given sufficient lead time and forecast skill, a portion of relief funds may be shifted from disaster recovery to disaster preparedness, reducing losses in lives and property. While short-term early warnings systems are commonplace, forecasts at the monthly or seasonal scale are relatively underused, despite their potential value. Incorporating both, numerous relief organizations have developed operational early action protocols for natural hazards. These plans may have well-defined forecasts, trigger criteria, and identification of early actions ranging from weeks to months prior to a predicted disaster, but many have not been explicitly optimized to maximize financial or utilitarian returns. This study investigates the effect o...
2
artículo
The Institute of Investigation magazine IIGEO, from the beginning, is dealing with relevant subjects in mining, the environment and sustainable development. Its incorporation as virtual magazine into the system of libraries (SISBIB) allowed to spread it over a large number of visitors. Subsequently, the magazine was entered into the Indexed Bases like LATINDEC and SCIELO PERU, achieving a major virtual presence in internet. In recent years, we worked for sharing OJS Base (Open Journal System). This initiative is promoted by the UNMSM Vice Rector of investigation in order to incorporate the university institutes of investigationpublications. IIGEO is considered among the best UNMSM virtual magazines like Annals of Medicine (Anales de Medicina), Biological Peruvian Magazine (Revista Peruana de Biología) and Peruvian Investigations of Veterinary Magazine (Revista de Investigaciones Peruana...
3
artículo
The Institute of Investigation magazine IIGEO, from the beginning, is dealing with relevant subjects in mining, the environment and sustainable development. Its incorporation as virtual magazine into the system of libraries (SISBIB) allowed to spread it over a large number of visitors. Subsequently, the magazine was entered into the Indexed Bases like LATINDEC and SCIELO PERU, achieving a major virtual presence in internet. In recent years, we worked for sharing OJS Base (Open Journal System). This initiative is promoted by the UNMSM Vice Rector of investigation in order to incorporate the university institutes of investigationpublications. IIGEO is considered among the best UNMSM virtual magazines like Annals of Medicine (Anales de Medicina), Biological Peruvian Magazine (Revista Peruana de Biología) and Peruvian Investigations of Veterinary Magazine (Revista de Investigaciones Peruana...
4
artículo
Publicado 2021
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In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and...
5
tesis doctoral
Publicado 2024
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This dissertation investigates the potential improvement of volcanic eruption understanding and forecasting methods by using advanced data processing techniques to analyze large datasets at three target volcanoes (Piton de la Fournaise (PdlF) (France), Sabancaya, and Ubinas (Peru)). The central objective of this study is to search for possible empirical relationships between the pre-eruptive behavior of the accelerated increase in seismic activity using the Failure Forecast Method (FFM) and velocity variations measured by Coda Wave Interferometry (CWI), since both observations are reported to be independently associated with medium damage. The FFM is a deterministic method used to forecast volcanic eruptions using an empirical relationship of increased and accelerated evolution of an observable (e.g., volcano-seismic event rates). The event rates used with FFM in this study were generate...
6
tesis doctoral
Publicado 2024
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This dissertation investigates the potential improvement of volcanic eruption understanding and forecasting methods by using advanced data processing techniques to analyze large datasets at three target volcanoes (Piton de la Fournaise (PdlF) (France), Sabancaya, and Ubinas (Peru)). The central objective of this study is to search for possible empirical relationships between the pre-eruptive behavior of the accelerated increase in seismic activity using the Failure Forecast Method (FFM) and velocity variations measured by Coda Wave Interferometry (CWI), since both observations are reported to be independently associated with medium damage. The FFM is a deterministic method used to forecast volcanic eruptions using an empirical relationship of increased and accelerated evolution of an observable (e.g., volcano-seismic event rates). The event rates used with FFM in this study were generate...
7
artículo
Publicado 2019
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Nowadays, the impacts of climate change are harming many countries around the world. For this reason, the scientific community is interested in improving methods to forecast weather events, so it is possible to avoid people from being injured. One important thing in the development of time series forecasting methods is to consider the set of values over time that facilitates the prediction of future value. In this sense, we propose a new feature vector based on the correlation and autocorrelation functions. These measures reflect how the observations of a time series are related to each other. Then, univariate forecasting is performed using Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) deep neural network. Finally, we compared the new model with linear and non-linear models. Reported results exhibit that MLP and LSTM models using the proposed feature vector, they show pro...
8
artículo
Publicado 2021
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In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and...
9
artículo
Publicado 2023
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The forecast of river stream flows is of significant importance for the development of early warning systems. Artificial intelligence algorithms have proven to be an effective tool in hydrological modeling data-driven, since they allow establishing relationships between input and output data of a watershed and thus make decisions data-driven. This article investigates the applicability of the k-nearest neighbor (KNN) algorithm for forecasting the mean daily flows of the Ramis river, at the Ramis hydrometric station. As input to the KNN machine learning algorithm, we used a data set of mean basin precipitation and mean daily flow from hydrometeorological stations with various lags. The performance of the KNN algorithm was quantitatively evaluated with hydrological ability metrics such as mean absolute percentage error (MAPE), anomaly correlation coefficient (ACC), Nash-Sutcliffe efficienc...
10
artículo
Publicado 2022
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Globally, the direct cost of natural disasters stands in the hundreds of billions of USD per year, at a time when water resources are under increasing stress and variability. Much of this burden rests on low- and middle-income countries that, despite their relative lack of wealth, exhibit considerable vulnerability such that losses measurably impact GDP. Within these countries, a growing middle class retains much of its wealth in property that may be increasingly exposed, while the few assets the poor may possess are often highly exposed. Vulnerability to extreme events is thus heterogeneous at both the global and subnational level. Moreover, the distribution and predictability of extreme events is also heterogeneous. Disaster managers and relief organizations are increasingly consulting operational climate information services as a way to mitigate the risks of extreme events, but approp...
11
artículo
Publicado 2021
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In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and...
12
artículo
Publicado 2016
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The present work has as objective to apply data mining techniques to develop a predictive model to forecast the chance of passing that will have a college student at the time of enrolling in a particular subject. Given that the academic record of the student can be known, and based on that information, we propose an Artificial Neural Network (ANN) that allows, using various configurations, to predict and assess our goal. The model has been applied to a compulsory subject of higher education of a University and given the results obtained. This model can be applied to any other subject analogous with satisfactory results.
13
artículo
Publicado 2019
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In the current market, there is a large number of SMEs that have a large margin of economic losses due to lack of stocks, due to the supply process. In other words, the lost sales and the costs of the services generated by not having their products available in their warehouses is a critical scenario in the distribution companies, whose added value lies in maximizing their level of customer service. To solve this problem, we propose a system that integrates the development of the attention and the model of the inventories of the periodic review, the bases based on the framework of the work. The results, after analyzing the demand, their patterns and choosing the best method to use, are antecedents to develop the management of inventories and their policies. Likewise, knowledge management will act as an integrated support. Through the simulation carried out for a distribution of lubricant...
14
artículo
Publicado 2020
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Streamflow forecasting at short horizons is vital for the management of water resources. However, the streamflow behaviour is non-linear and not stationary. To address this challenge, artificial intelligence techniques have been used to increase accuracy. Additionally, signal decomposition techniques such as empirical mode decomposition, ensemble empirical mode decomposition, and variational mode decomposition, have been applied in different fields as a pre-processing stage prior to modelling to improve forecasting. This study evaluates the effect of the aforementioned decomposition techniques used with a recurrent neural network called long short-Term memory to increase the precision of the daily prediction of the Chira river streamflow in northern Peru, characterized by a special dynamic due to a strong seasonal behavior and the influence of the El Niño-Southern Oscillation (ENSO). ©...
15
artículo
Publicado 2023
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Within the commercial sector, SMEs represent more than 90% of all companies; they are responsible for 50% of GDP and generate between 60% and 70% of employment worldwide, which is why they are critical in the Peruvian economy. However, through an exhaustive review of the literature and sectoral analysis, we concluded they have a high risk of failure in the short term due to various problems, such as poor inventory management. In Peru, the provisions for carrying out inventories usually have a ratio of between 1% and 1.4% of the total inventory stock; thus, SMEs belonging to the hardware sector more frequently present this problem that affects the profitability of their companies. For this reason, the need arises to design an inventory control model that increases the productivity of hardware SMEs. After the pilot implementation of the first component, an increase in distribution efficien...
16
objeto de conferencia
Publicado 2024
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University students experiment different factors that bring as a consequence the abandonment of his professional career. In Perú, the dropout rate becomes a critical point of attention due to its increase since COVID-19. Despite the fact that the institutions join forces to improve student retention, these seem to be insufficient because of the root causes of the problem are not analyzed. Hence, this study aims to analyze the main causes associated to student dropout of a population of students from the academic period 2022-2 of a private university. For this purpose, three predictive models (random forest, logistic regression and decision tree) were designed to identify the main risks associated to abandonment of students. The predictive models were designed with the automatic learning method (Machine Learning) through Google Collab programming, obtaining a comparison of predicted drop...
17
tesis de grado
Publicado 2024
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Hoy en día, es crucial pronosticar con precisión los productos, especialmente para una empresa que importa sus productos. Disponer de una previsión precisa permite a la empresa optimizar la gestión de recursos, aumentando la productividad y evitando la sobreventa o subventa de productos. Además, establecer un modelo de planificación de materiales basado en la demanda es esencial para garantizar que nuestros proveedores cumplan con sus compromisos de nivel de servicio. En este proyecto de investigación, se emplean Machine Learning y Big Data para mejorar los métodos de previsión de las empresas de bienes de consumo. Se han entrenado los datos recopilados de las ventas de la empresa durante los últimos cuatro años para “la categoría de cabello” y se empleará el método Arima para predecir los primeros 8 meses del año 2023. Además, el Plan de requisitos de materiales impu...
18
artículo
Publicado 2023
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This article details the process that was carried out for the salary forecast in a database of a census given in 1996, where the Python programming language was used, for the analysis of the data of the dataset the Google Colab server was used to execute the algorithms in the cloud, since the team considered that the speed of data analysis in Google Colab is faster. One of the data mining techniques was also used to classify the variables using decision trees that have the ability to graphically represent several alternative solutions in order to determine the most effective courses/routes of action for the classification of the obtainment. of a person's salary.
19
artículo
A method is proposed to analyze data generated by a family of stochastic processes called autoregressive conditional heteroscedastic processes (ARCH), which are widely used to predict volatility of financial time series. An ARCE model is used to predict the volatility of the Atacocha mining company stock price based on the data from 1992 to 2003.
20
artículo
Publicado 2023
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The dengue virus has become an increasingly critical problem for humanity due to its extensive spread. This is transmitted through a vector that sprouts in certain climatic conditions (tropical and subtropical climates). The transmission of the disease can be associated with certain climatic variables that reinforce the outbreak. Data were collected on dengue cases by epidemiological week registered in Loreto-Peru from January 1, 2016, to January 31, 2022. Likewise, data on meteorological variables (maximum and minimum temperature; dry and humid bulb temperature; wind speed and total precipitation in the area). In this study, four Machine learning modeling techniques were considered: Support Vector Machine (SVM), Decision Tree, Random Forest and AdaBoost; and the parameters defined to evaluate the models are: Accuracy, Precision, Recall and F-1. As a result, optimal AUC values were obtai...
