Climate Change Shocks Exposure Index to Drought on the Livelihoods of the Smallholder Farmers in Kinakomba Ward, Tana River County, Kenya

Being susceptible to climate change means being unable to cope with the adverse effects of climate change especially droughts and a likelihood of experiencing harm due to its occurrence. The study sought to evaluate the effects of exposure to Climate related shocks on the livelihoods of the smallholder farmers with the intent of formulating appropriate policies to enable them cope with its impacts. A descriptive survey research design was used. Stratified random sampling was employed to select 390 households. Two methods were used to analyse exposure. Firstly the fuzzy logic in assessing susceptibility to drought involving a selection of input variables, Fuzzification, inference modelling and defuzzification and secondly DrinC Model software. The results revealed that the final value of the negative consequences of drought was 0.35.The study also established a single index as 0.45 for exposure for the entire study period of 35 years for Kinakomba Ward .The study showed that exposure was statistically significant at (0.000066). The study further revealed that the periods between occurrence of extreme droughts were reducing and at the same time that droughts were moving from being severe to being extreme within shorter periods of time leaving smallholder farmers who depend on rain fed agriculture with high exposures and risks as well as experiencing longer hunger periods with severe implications on their food and nutritional security for the vast populations in the study area. The Study concluded that the exposure to drought of the smallholder farmers in Kinakomba Ward is significantly related to their farming livelihood systems. This study recommends that the County Government in partnership with the National Government and other stakeholders develop a comprehensive disaster risk management framework to address the drought hazards and undertake mitigation and adaptation measures by equipping the smallholder farmers with knowledge on how to cope with the cyclic and vicious droughts’ impacts that have led to serious irreversible harm to humans and livestock in the area. Keywords: Exposure, Drought, Mitigation, Adaptation, Food security DOI: 10.7176/FSQM/94-07 Publication date: February 29th 202

Adapting a quality model for a Big Data application: the case of a feature prediction system

En la última década hemos sido testigos del considerable incremento de proyectos basados en aplicaciones de Big Data. Algunos de los tipos más populares de esas aplicaciones han sido: los sistemas de recomendaciones, la predicción de características y la toma de decisiones. En este nuevo auge han surgido propuestas de implementación de modelos de calidad para las aplicaciones de Big data que por su gran heterogeneidad se hace difícil la selección del modelo de calidad ideal para el desarrollo de un tipo específico de aplicación de Big Data. En el presente Trabajo de Fin de Máster se realiza un estudio de mapeo sistemático (SMS, por sus siglas en inglés) que parte de dos preguntas clave de investigación. La primera trata sobre cuál es el estado en la identificación de riesgos, problemas o desafíos en las aplicaciones de Big Data. La segunda, trata sobre qué modelos de calidad se han aplicado hasta la fecha a las aplicaciones de Big Data, específicamente a los sistemas de predicción de características. El objetivo final es analizar los modelos de calidad disponibles y adaptar un modelo de calidad a partir de los existentes que se puedan aplicar a un tipo específico de aplicación de Big Data: los sistemas de predicción de características. El modelo definido comprende un conjunto de características de calidad definidas como parte del modelo y métricas de calidad para evaluarlas. Finalmente, se realiza una aproximación a un caso de estudio donde se aplica el modelo y se evalúan las características de calidad definidas a través de sus métricas de calidad presentándose los resultados obtenidos.In the last decade, we have been witnesses of the considerable increment of projects based on big data applications. Some of the most popular types of those applications have been: Recommendations, Feature Predictions, and Decision making. In this new context, several proposals have arisen for the implementation of quality models applied to Big Data applications. As part of the current Master thesis, a Systematic Mapping Study (SMS) is conducted which starts from two key research questions. The first one is about what is the state of the art about the identification of risks, issues, problems, or challenges in big data applications. The second one, is about which quality models have been applied up to date to big data applications, specifically to feature prediction systems. The main objective is to analyze the available quality models and adapt a quality model from the existing ones that can be applied to a specific type of Big Data application: The Feature Prediction Systems. The defined model comprises a set of quality characteristics defined as part of the model and a set of quality metrics to evaluate them. Finally, an approach is made to a case study where the model is applied, and the quality characteristics defined through its quality metrics are evaluated. The results are presented and discussed.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Máster en Ingeniería Informátic