PhDH / Department of Hydrology and Water Resources / Demand for water resources has been on the increase and is compounded by population growth and related development demands. Thus, numerous sectors are affected by water scarcity and therefore effective management of drought-induced water deficit is of importance. Luvuvhu River Catchment (LRC), a tributary of the Limpopo River Basin in South Africa has witnessed an increasing frequency of drought events over the recent decades. Drought impacts negatively on communities’ livelihoods, development, economy, water resources, and agricultural yields. Drought assessment in terms of frequency and severity using Drought Indices (DI) in different parts of the world has been reported. However, the forecasting and prediction component which is significant in drought preparedness and setting up early warning systems is still inadequate in several regions of the world. This study aimed at characterising, assessing, and predicting drought conditions using DI as a drought quantifying parameter in the LRC. This was achieved through the application of hybrid statistical and machine learning models including predictions via a combination of hybrid models. Rainfall and temperature data were obtained from South African Weather Service, evapotranspiration, streamflow, and reservoir storage data were obtained from the Department of Water and Sanitation while root zone soil moisture data was derived from the NASA earth data Giovanni repository. The Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Streamflow Index (SSI), and Nonlinear Aggregated Drought Index (NADI) were selected to assess and characterise drought conditions in the LRC. SPI is precipitation based, SPEI is precipitation and evapotranspiration based, SSI is based on streamflow while NADI is a multivariate index based on rainfall, potential evapotranspiration, streamflow, and storage reservoir volume. All indices detected major historical drought events that have occurred and reported over the study area, although the precipitation based indices were the only indices that categorised the 1991/1992 drought as extreme at 6- and 12- month timescales while the streamflow index and multivariate NADI underestimated the event. The most recent 2014/16 drought was also categorised to be extreme by the standardised indices. The study found that the multivariate index underestimates most historical drought events in the catchment. The indices further showed that the most prevalent drought events in the LRC were mild drought. Extreme drought events were the least found at 6.42%, 1.08%, 1.56%, and 4.4% for SPI, SPEI, SSI, and NADI, respectively. Standardised indices and NADI showed negative trends and positive upward trends, respectively. The positive trend showed by NADI depicts a decreased drought severity over the study period. Drought events were characterised based on duration, intensity, severity, and frequency of drought events for each decade of the 30 years considered in this study i.e. between 1986 – 1996, 1996 – 2006, 2006 – 2016. This was done to get finer details of how drought characteristics behaved at a 10-year interval over the study period. An increased drought duration was observed between 1986 - 1996 while the shortest duration was observed between 1996 - 2006 followed by 2006 - 2016. NADI showed an overall lowest catchment duration at 1- month timescale compared to the standardised indices. The relationship between drought severity and duration revealed a strong linear relationship across all indices at all timescales (i.e. an R2 of between 0.6353 and 0.9714, 0.6353 and 0.973, 0.2725 and 0.976 at 1-, 6- and 12- month timescales, respectively). In assessing the overall utilisation of an index, the five decision criteria (robustness, tractability, transparency, sophistication, and extendibility) were assigned a raw score of between one and five. The sum of the weighted scores (i.e. raw scores multiplied by the relative importance factor) was the total for each index. SPEI ranked the highest with a total weight score of 129 followed by the SSI with a score of 125 and then the SPI with a score of 106 while NADI scored the lowest with a weight of 84. Since SPEI ranked the highest of all the four indices evaluated, it is regarded as an index that best describes drought conditions in the LRC and was therefore used in drought prediction. Statistical (GAM-Generalised Additive Models) and machine learning (LSTM-Long Short Term Memory) based techniques were used for drought prediction. The dependent variables were decomposed using Ensemble Empirical Mode Decomposition (EEMD). Model inputs were determined using the gradient boosting, and all variables showing some relative off importance were considered to influence the target values. Rain, temperature, non-linear trend, SPEI at lag1, and 2 were found to be important in predicting SPEI and the IMFs (Intrinsic Mode Functions) at 1, 6- and 12- month timescales. Seven models were applied based on the different learning techniques using the SPEI time series at all timescales. Prediction combinations of GAM performed better at 1- and 6- month timescales while at 12- month, an undecomposed GAM outperformed the decomposition and the combination of predictions with a correlation coefficient of 0.9591. The study also found that the correlation between target values, LSTM, and LSTM-fQRA was the same at 0.9997 at 1- month and 0.9996 at 6- and 12- month timescales. Further statistical evaluations showed that LSTM-fQRA was the better model compared to an undecomposed LSTM (i.e. RMSE of 0.0199 for LSTM-fQRA over 0.0241 for LSTM). The best performing GAM and LSTM based models were used to conduct uncertainty analysis, which was based on the prediction interval. The PICP and PINAW results indicated that LSTM-fQRA was the best model to predict SPEI timeseries at all timescales. The conclusions drawn from drought predictions conducted in this study are that machine learning neural networks are better suited to predict drought conditions in the LRC, while for improved model accuracy, time series decomposition and prediction combinations are also implementable. The applied hybrid machine learning models can be used for operational drought forecasting and further be incorporated into existing early warning systems for drought risk assessment and management in the LRC for better water resources management. Keywords: Decomposition, drought, drought indices, early warning system, frequency, machine learning, prediction intervals, severity, water resources. / NRF

Only 70% of the Swedish students graduate from upper secondary school within the given time frame. Earlier research has shown that unfinished degrees disadvantage the individual student, policy makers and society. A first step for preventing dropouts is to indicate students about to fail courses. Thus the purpose is to identify tendencies whether a student will pass or not pass a course. In addition, the thesis accounts for the development of an Early Warning System to be applied to signal which students need additional support from a professional teacher. The used algorithm Random Forest functioned as a binary classification model of a failed grade against a passing grade. Data in the study are in samples of approximately 700 students from an upper secondary school within the Stockholm municipality. The chosen method originates from a Design Science Research Methodology that allows the stakeholders to be involved in the process. The results showed that the most dominant indicators for classifying correct were Absence, Previous grades and Mathematics diagnosis. Furthermore, were variables from the Learning Management System predominant indicators when the system also was utilised by teachers. The prediction accuracy of the algorithm indicates a positive tendency for classifying correctly. On the other hand, the small number of data points imply doubt if an Early Warning System can be applied in its current state. Thus, one conclusion is in further studies, it is necessary to increase the number of data points. Suggestions to address the problem are mentioned in the Discussion. Moreover, the results are analysed together with a review of the potential Early Warning Systemfrom a didactic perspective. Furthermore, the ethical aspects of the thesis are discussed thoroughly. / Endast 70% av svenska gymnasieelever tar examen inom den givna tidsramen. Tidigare forskning har visat att en oavslutad gymnasieutbildning missgynnar både eleven och samhället i stort. Ett första steg mot att förebygga att elever avviker från gymnasiet är att indikera vilka studenter som är på väg mot ett underkänt betyg i kurser. Därmed är syftet med rapporten att identifiera vilka trender som bäst indikerar att en elev kommer klara en kurs eller inte. Dessutom redogör rapporten för utvecklandet av ett förebyggande varningssystem som kan appliceras för att signalera vilka studenter som behöver ytterligare stöd från läraren och skolan. Algoritmen som användes var Random Forest och fungerar som en binär klassificeringsmodell av ett underkänt betyg mot ett godkänt. Den data som använts i studien är datapunkter för ungefär 700 elever från en gymnasieskola i Stockholmsområdet. Den valda metoden utgår från en Design Science Researchmetodik vilket möjliggör för intressenter att vara involverade i processen. Resultaten visade att de viktigaste variablerna var frånvaro, tidigare betyg och resultat från Stockholmsprovet (kommunal matematikdiagnos). Vidare var variabler från lärplattformen en viktig indikator ifall lärplattformen användes av läraren. Algoritmens noggrannhet indikerade en positiv trend för att klassificeringen gjordes korrekt. Å andra sidan är det tveksamt ifall det förebyggande systemet kan användas i sitt nuvarande tillstånd då mängden data som användes för att träna algoritmen var liten. Därav är en slutsats att det är nödvändigt för vidare studier att öka mängden datapunkter som används. I Diskussionen nämns förslag på hur problemet ska åtgärdas. Dessutom analyseras resultaten tillsammans med en utvärdering av systemet från ett didaktiskt perspektiv. Vidare diskuteras rapportens etiska aspekter genomgående.

Over the past decades, public concern about global environmental change has grown, following the progressive increase in both frequency and intensity of extreme events. Even though the problem is global, it has proved to have very different societal and environmental impacts at local level, further widening the gap between disadvantaged and advantaged communities, according to the degree of vulnerability of their social, economic and environmental systems. Among the various anthropogenic activities, the agricultural sector is particularly linked to global environmental change by a two-way relationship: on the one hand, intensive mono-cultures, together with intensive livestock production, compromise the environment and produce huge CO$_2$ emissions (one of the most important factors behind global warming); on the other hand, smallholder farming is one of the most endangered sectors by global environmental change, precisely because it depends heavily on the natural resources of the territory, including favourable weather and climate. Scientific research, supported by international institutions, has been working on this subject for several decades, analysing phenomena at global and local scale and providing medium and long-term forecasts capable of directing economic and political strategies. Such complex investigations become even more complex in contexts lacking reliable environmental data, where their low-quality and low representativeness weaken their reliability, compromising the reliability of the outcomes as well. This thesis seeks to respond to the increasing need of realistically addressing environmental phenomena that threaten rural communities and the environment on which they depend in low-income countries, by investigating two of the main environmental factors affecting tropical farming practices: river-floodplain dynamics and climate change. Despite data-related constraints, the environment of tropical rural areas still provides a unique opportunity to study several near-natural processes, such as the morphodynamics of mostly free-flowing rivers. Especially in foothill regions, unconfined or partially confined conditions of tropical rivers allow evaluating the natural dynamics of erodible river corridors, with erosion and accretion shaping their interactions with the adjacent floodplain and related human activities. At the same time, the complex terrain characterizing the river valleys at the foothills of high mountain chains also offers the opportunity to study interesting local meteorological processes, especially considering the interaction between synoptic-scale dynamics and local convective phenomena. In this context, local bottom-up initiatives and new and tailored-to-context strategies for adaptation to the ongoing environmental change are deepened following a multidisciplinary approach. This PhD research has been framed within an international cooperation project entitled “Sustainable Development and Fight against Climate Change in the Upper Huallaga basin (Peru)”, promoted by Mandacarù ONLUS, and funded by the Autonomous Province of Trento. The project aimed to enhance the resilience of the local farmers of the Upper Huallaga valley (Peru), facing the consequences of climate change and implementing new agricultural initiatives with a special attention to plantain and banana fields. Thanks to the support of the involved partners (Redesign by PROMER s.a.c., the Universidad Agraria Nacional de la Selva de Tingo Maria, in Peru, and the Edmund Mach Foundation of San Michele all’Adige, in Italy), the project provided the opportunity to carry out a consistent set of fieldwork activities over an 8-months period collecting hydro-morphological data, interviewing the local population, and installing two weather stations. The PhD thesis has been structured along two main parts, related to to the assessment of climate change effects on local agricultural practices, and the interplay between river-floodplain dynamics and floodplain agriculture. The part on the assessment of climate change includes two main research elements. First, a novel approach is used to evaluate climate change in data-scarce contexts: non-conventional data sources (population survey) are compared with conventional data sources (few local historical weather stations and global reanalysis data series – ERA5), to better account for the sub-daily time scale (local conventional sources only provide daily data), correlating weather changes perceived by farmers (more thunderstorms and longer drought periods) with climate variations deduced from quantitative data. Second, after having determined the most impacting meteorological variables on crops through the survey, a weather early-warning system has been developed to provide agro-meteorological forecasts to the \textit{bananeros} (banana farmers) of the Upper Huallaga valley. The system, based on the Weather Research and Forecasting (WRF) model, and enhanced with the assimilation of real-time observations from local meteorological stations installed during the project fieldwork, issues an alert when the predicted wind speed exceeds thresholds related to potential damage to the harvest, and spreads the warning via text messages. Such alerting system contains several novel features in relation to the socio-environmental context, allowing to discuss its potential for replication in analogous, vulnerable situations. The part on river-floodplain dynamics also includes two main research elements. First, a remote-sensing analysis is conducted at reach scale in two different reaches of the Huallaga River, quantifying geomorphological river trajectories and land use changes in the adjacent floodplain. The outcomes show that river morphology reacts differently depending on the agricultural systems (extensive or intensive) in the nearby floodplain, revealing a high geomorphological sensitivity of such a near-natural, highly dynamic river reach. Second, riverine agriculture within the erodible river corridor is analysed in association with riverine islands dynamics, at the geomorphic unit scale, evaluating the morphological evolution and agricultural suitability of two cultivated fluvial islands. The three main drivers of agricultural suitability within river erodible corridors, i.e. river disturbance, cultivation windows of opportunity, and soil suitability are quantified, allowing to generalize a process-based conceptual model of riverine islands as complex-adaptive-systems.

La crise alimentaire de 2008 a suscité un regain d’intérêt pour les questions agricoles et de sécurité alimentaire dans les pays en développement. Partant du constat que près de 27% de la population d’Afrique Sub-saharienne souffre de malnutrition, cette thèse a pour objectif de contribuer à une meilleure compréhension des causes complexes de l’insécurité alimentaire. Le premier chapitre étudie les mécanismes de transmission des variations du prix mondial du riz aux prix domestiques dans trois pays ouest-africain: le Sénégal, le Tchad et le Mali. Les résultats indiquent que le prix du riz importé à Dakar et le prix du riz local à Bamakorépondent de façon asymétrique aux variations du prix mondial. Le chapitre 2 teste la présence d’obstacles aux échanges agricoles entre pays d’Afrique de l’Ouest et du Centre. Il ressort de l’analyse que le passage des frontières est coûteux. Toutefois, le coût associé au passage de la frontière est plus faible entre pays membre d’une même union économique et monétaire. Le chapitre 3 a pour objectif le renforcement des systèmes d’alertes précoces des crises alimentaires existants au Sahel. Il montre qu’il est possible d’anticiper les crises de prix avec six mois d’avance en analysant les mouvements passés des prix des céréales. Enfin, le chapitre 4 s’intéresse à la vulnérabilité des ménages face aux chocs pluviométriques. Il révèle que les ménages ruraux au Burkina Faso n’ont pas la capacité d’assurer ou d’absorber ces chocs climatiques. / This doctoral thesis is in line with the renewed interest in research on agriculture and food security, following the 2008 global food crisis. The aim of this thesis is to contribute to a better understanding of the complex issues surrounding food security. The first chapter investigates whether the changes in the international price of rice are transmitted to the domestic prices of rice in Senegal, Mali and Chad. Results indicate that the domestic prices of imported rice in Dakar and of local rice in Bamako react differently to changes in the world price depending on whether the world price is rising or falling. Chapter 2 analyses by how much trade barriers at the border and transport costs impede the integration of agricultural markets in West and Central Africa. Results highlight the role played by borders in explaining price deviations between markets. Additionally, belonging to an economic union and sharingthe same currency appear as major determinants of market integration. The third chapter aims at providing new early warning indicators based on food prices in Mali, Niger and Burkina Faso. Our analysis reveals that price crisis can be predicted about 6 months in advance through the observation of past price movements. Chapter 4 focuses on the analysis of children’s vulnerability to climate shocks in Burkina Faso. By combining health data originating from a 2008 household survey with meteorological data, we show the importance of weather conditions in prenatal period and in the first year of life on the future nutritional status of the children.

Practitioners of Forecast-based Action (FbA) argue that a humanitarian response able to utilize forecasts to accurately predict disaster, secure funding, and take action before the onset of a crisis will benefit donors and beneficiaries alike. In search of effective and efficient early-action regimes, a number of major humanitarian actors are developing FbA projects of various designs, predominantly in response to natural disaster and famine. While numerous organizations and institutions have expressed interest in developing FbA mechanisms, the tool has only been applied in a limited capacity to the humanitarian needs generated by armed conflict. This research seeks to understand whether a scalable FbA approach can be developed to stage principled, anticipatory humanitarian action in response to situations in which rigorous evaluations predict the likelihood of imminent armed conflict. The hypothesis is that the application of FbA to armed conflict is possible, but due to the complex political nature of conflict, implementing organizations should try to focus on creating mechanisms managed by humanitarian actors and, in so far as possible, be insulated from outside influence. This research is the first academic work to specifically investigate the application of FbA to armed conflict. Following an extensive review of current FbA mechanisms and conflict early warning practices, this research concludes that a conflict-centered FbA system akin to the automated FbA systems in use today to respond to natural disaster and famine is possible, but that the endeavor presents many practical and conceptual barriers to implementation. In particular, diffuse models such as the Start Fund offer a hopeful glimpse at a type of horizontal, member-driven FbA mechanism that is both highly context-sensitive and relatively insulated from outside influence. Such a design, however, features notable and inherent limitations in its ability to reliably and accurately predict the outbreak of conflict and respond in a manner that minimizes regretful actions.

Connus sous le nom de Systèmes d’Alerte Avancés, ou Early Warning Systems (EWS), les modèles de prévision des crises financières sont appelés à jouer un rôle déterminant dans l’orientation des politiques économiques tant au niveau microéconomique qu’au niveau macroéconomique et international. Or,dans le sillage de la crise financière mondiale, des questions majeures se posent sur leur réelle capacité prédictive. Deux principales problématiques émergent dans le cadre de cette littérature : comment évaluer les capacités prédictives des EWS et comment les améliorer ?Cette thèse d’économétrie appliquée vise à proposer (i) une méthode d’évaluation systématique des capacités prédictives des EWS et (ii) de nouvelles spécifications d’EWS visant à améliorer leurs performances. Ce travail comporte quatre chapitres. Le premier propose un test original d’évaluation des prévisions par intervalles de confiance fondé sur l’hypothèse de distribution binomiale du processus de violations. Le deuxième chapitre propose une stratégie d’évaluation économétrique des capacités prédictives des EWS. Nous montrons que cette évaluation doit être fondée sur la détermination d’un seuil optimal sur les probabilités prévues d’apparition des crises ainsi que sur la comparaison des modèles.Le troisième chapitre révèle que la dynamique des crises (la persistance) est un élément essentiel de la spécification économétrique des EWS. Les résultats montrent en particulier que les modèles de type logit dynamiques présentent de bien meilleurs capacités prédictives que les modèles statiques et que les modèles de type Markoviens. Enfin, dans le quatrième chapitre nous proposons un modèle original de type probit dynamique multivarié qui permet d’analyser les schémas de causalité intervenant entre différents types crises (bancaires, de change et de dette). L’illustration empirique montre clairement que le passage à une modélisation trivariée améliore sensiblement les prévisions pour les pays qui connaissent les trois types de crises. / Known as Early Warning Systems (EWS), financial crises forecasting models play a key role in definingeconomic policies at microeconomic, macroeconomic and international level. However, in the wake ofthe global financial crisis, numerous questions with respect to their forecasting abilities have been raised,as very few signals were drawn prior to the starting of the turmoil. Two questions arise in this context:how to evaluate EWS forecasting abilities and how to improve them?The broad goal of this applied econometrics dissertation is hence (i) to propose a systematic model-free evaluation methodology for the forecasting abilities of EWS as well as (ii) to introduce new EWSspecifications with improved out-of-sample performance. This work has been concretized in four chapters.The first chapter introduces a new approach to evaluate interval forecasts which relies on the binomialdistributional assumption of the violations series. The second chapter proposes an econometric evaluationmethodology of the forecasting abilities of an EWS. We show that adequate evaluation must take intoaccount the cut-off both in the optimal crisis forecast step and in the model comparison step. The thirdchapter points out that crisis dynamics (persistence) is essential for the econometric specification of anEWS. Indeed, dynamic logit models lead to better out-of-sample forecasting probabilities than those oftheir main competitors (static model and Markov-switching one). Finally, a multivariate dynamic probitEWS is proposed in the fourth chapter to take into account the causality between different types of crises(banking, currency, sovereign debt). The empirical application shows that the trivariate model improvesforecasts for countries that underwent the three types of crises.

L’objectif de ce travail est d’analyser les implications du choix de régime de change dans les pays émergents et en développement, ainsi que d’apporter des éclaircissements sur les facteurs jouant un rôle important dans le déclenchement des crises (de change, bancaires, financières…) dans ces pays. Pour cela, l’analyse se tourne, dans un premier temps, vers la question du choix de régime de change optimal. Cette partie du travail s’appuie principalement sur un grand nombre de travaux théoriques et empiriques traitant de cette question, pour mettre en lumière les implications de ce choix, tout en tenant compte des particularités du groupe de pays qui font l’objet de cette étude. Dans une deuxième partie nous nous intéressons aux crises et les facteurs qui jouent un rôle majeur dans leur incidence. Ainsi, après une revue des différents modèles de crises afin d’identifier les variables d’intérêt, nous construisons deux modèles de prédiction des crises, ou « d’alarme précoce ». Enfin, la troisième partie du travail rassemble les enseignements tirés des deux parties précédentes pour traiter d’une question qui prend une ampleur croissante dans ces pays : étant donné la logique d’intégration financière mondiale et les avantages présentés par un régime de changes flottants dans un tel contexte, de quelle manière un pays envisageant un sortie vers ce régime de change peut-il la planifier, et à quel moment doit-il l’entreprendre, pour réussir une sortie sans crise majeure, que nous qualifions de sortie « ordonnée » ? Pour répondre à cette question, nous nous appuyons sur des expériences passées qui nous permettent de construire un modèle identifiant les variables susceptibles d’accroître la probabilité d’une sortie ordonnée. Nous complétons ce modèle par quelques considérations supplémentaires qui constituent des conditions importantes à la réussite d’une sortie ordonnée. L’objectif est d’apporter des recommandations susceptibles de faciliter cette transition. / The aim of this work is to analyze the implications of exchange rate regime choice in developing and emerging market economies, as well as highlight the factors that play a major role in the incidence of crises (currency, banking, financial…) in these countries. With this aim in mind, we start our analysis by turning to the question of the choice of the optimal exchange rate regime. This part of our work draws on a large number of both theoretical and empirical works evoking this question in order to determine the implications of this choice, all the while keeping in mind the fact that this particular group of countries present certain characteristics that are usually absent in their industrial counterparts. The second part of our work concentrates more specifically on crises and the factors that play a major role in their occurrence. Therefore, following a brief overview of different crisis models in order to identify the variables of interest, we propose two models for crisis prediction, or “Early Warning Systems”. Finally, the third and final part of our work brings together the conclusions of the earlier parts in order to address an issue that is becoming increasingly important in developing and emerging market economies: given their greater integration in international financial and capital markets, as well as the incontestable advantages of a floating exchange rate regime in such a context, how can a country wishing to exit to a more flexible exchange rate arrangement undertake such a transition, and when, in order to achieve an “orderly” exit, that is, an exit that is not accompanied by a crisis? To answer this question we draw on past experiences to construct a model indentifying the economic variables that might increase the probability of an orderly exit. We complete this model with a number of additional considerations that have recently emerged as important preconditions for an orderly exit, in order to provide some useful policy recommendations facilitating this transition.

La récente crise financière qui a débuté aux États-Unis en 2007 a révélé les faiblesses du système bancaire international se traduisant par l’effondrement de nombreuses institutions financières aux États-Unis et aussi par l’augmentation de la part des prêts non performants dans les bilans des banques européennes. Dans ce cadre, nous proposons d’abord d’estimer et de tester l’efficacité des modèles de prévisions des défaillances bancaires. L’objectif étant d’établir un système d’alerte précoce (EWS) de difficultés bancaires basées sur des variables financières selon la typologie CAMEL (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). Dans la première étude, nous avons comparé la classification et la prédiction de l’analyse discriminante canonique (CDA) et de la régression logistique (LR) avec et sans coûts de classification en combinant ces deux modèles paramétriques avec le modèle descriptif d’analyse en composantes principales (ACP). Les résultats montrent que les modèles (LR et CDA) peuvent prédire la faillite des banques avec précision. De plus, les résultats de l’ACP montrent l’importance de la qualité des actifs, de l’adéquation des fonds propres et de la liquidité en tant qu’indicateurs des conditions financières de la banque. Nous avons aussi comparé la performance de deux méthodes non paramétriques, les arbres de classification et de régression (CART) et le nouveau modèle régression multivariée par spline adaptative (MARS), dans la prévision de la défaillance. Un modèle hybride associant ’K-means clustering’ et MARS est également testé. Nous cherchons à modéliser la relation entre dix variables financières et le défaut d’une banque américaine. L’approche comparative a mis en évidence la suprématie du modèle hybride en termes de classification. De plus, les résultats ont montré que les variables d’adéquation du capital sont les plus importantes pour la prévision de la faillite d’une banque. Enfin, nous avons étudié les facteurs déterminants des prêts non performants des banques de l’Union Européenne durant la période 2012-2015 en estimant un modèle à effets fixe sur données de panel. Selon la disponibilité des données nous avons choisi un ensemble de variables qui se réfèrent à la situation macroéconomique du pays de la banque et d’autres variables propres à chaque banque. Les résultats ont prouvé que la dette publique, les provisions pour pertes sur prêts, la marge nette d’intérêt et la rentabilité des capitaux propres affectent positivement les prêts non performants, par contre la taille de la banque et l’adéquation du capital (EQTA et CAR) ont un impact négatif sur les créances douteuses. / The recent financial crisis that began in the United States in 2007 revealed the weaknesses of the international banking system resulting in the collapse of many financial institutions in the United States and also the increase in the share of non-performing loans in the balance sheets of European banks. In this framework, we first propose to estimate and test the effectiveness of banking default forecasting models. The objective is to establish an early warning system (EWS) of banking difficulties based on financial variables according to CAMEL’s ratios (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). In the first study, we compared the classification and the prediction of the canonical discriminant analysis (CDA) and the logistic regression (LR) with and without classification costs by combining these two parametric models with the descriptive model of principal components analysis (PCA). The results show that the LR and the CDA can predict bank failure accurately. In addition, the results of the PCA show the importance of asset quality, capital adequacy and liquidity as indicators of the bank’s financial conditions. We also compared the performance of two non-parametric methods, the classification and regression trees (CART) and the newly multivariate adaptive regression splines (MARS) models, in the prediction of failure. A hybrid model combining ’K-means clustering’ and MARS is also tested. We seek to model the relationship between ten financial variables (CAMEL’s ratios) and the default of a US bank. The comparative approach has highlighted the supremacy of the hybrid model in terms of classification. In addition, the results showed that the capital adequacy variables are the most important for predicting the bankruptcy of a bank. Finally, we studied the determinants of non-performing loans from European Union banks during the period 2012-2015 by estimating a fixed effects model on panel data. Depending on the availability of data we have chosen a set of variables that refer to the macroeconomic situation of the country of the bank and other variables specific to each bank. The results showed that public debt, loan loss provisions, net interest margin and return on equity positively affect non performing loans, while the size of the bank and the adequacy of capital (EQTA and CAR) have a negative impact on bad debts.

PhD (Geography) / Department of Geography and Geo-Information Sciences / Malaria is a climate-change concatenated biological hazard that may, like any other natural hazard, can lead to a disaster if there is a failure in handling emergencies or risks. A holistic solution for malaria mitigation can be provided when indigenous knowledge is complemented with scientific knowledge. Malaria remains a challenge in South Africa and Limpopo province is the highest burdened malaria-endemic region. Specifically, Vhembe District is the highest burdened followed by Mopani District (Raman et al., 2016). This research sought to mitigate malaria transmissions in Mopani District through the integration of indigenous and scientific knowledge. The study was carried out in Mopani District of South Africa and 4 municipalities were involved. These are Ba-Phalaborwa, Greater Tzaneen, Greater Letaba, and Maruleng. A pragmatism philosophy was adopted hence the study took a mixed approach (sequential multiphase design). Data was collected from 381 selected participants through in-depth interviews, a survey and a focus group discussion. Participants for the in-depth interviews were obtained through snowballing and selected randomly for the survey, while for the focus group discussion purposive sampling was used. The study applied constructivist grounded theory to analyze qualitative data and to generate theory. Statistical Package for Social Sciences version 23.0 was used for quantitative data. Based on empirical findings, it was concluded that temperature and rainfall among other various factors exacerbate malaria transmission in the study area. Results of the study also show that people in Mopani District predict the malaria season onset by forecasting rainfall using various indigenous knowledge based indicators. The rainfall indicators mentioned by participants in the study were used in the developed early warning system. An Early warning system is an essential tool that builds the capacities of communities so that they can reduce their vulnerability to hazards or disasters. In the design of the system, Apache Cordova, JDK 1.8, Node JS, and XAMPP software were used. The study recommends malaria management and control key stakeholders to adopt the developed early warning system as a further mitigation strategy to the problem of malaria transmission in Mopani District. / NRF

本研究分析我國49家本國銀行、55家信用合作社、287家農會信用部及27家漁會信用部等四類金融機構之經營現況，並參照各國金融預警制度運作方式，選取適合的財務比率，運用SAS統計軟體及Z-score、Logistic等模型，分別找出造成各類金融機構經營失敗之顯著相關財務比率，評估各類金融機構之經營效率、失敗機率與模型之正確區別率，以建立預測金融機構失敗機率之預警模型。研究之樣本資料分別為：本國銀行49家、2001年第2季~2003年底共計11季25項財務比率，信用合作社55家、1998年底~2003年底共計21季26項財務比率，農會信用部287家1998年底~2003年底共計21季25項財務比率，漁會信用部27家1998年底~2003年底共計21季25項財務比率。 本研究之結論為： 一、彙整Z-Score模型對各類金融機構具有顯著性之財務變數，本國銀行有6項、信用合作社有7項、農會信用部有6項，漁會信用部有4項。 二、彙整Logistic模型對各類金融機構具有顯著性之財務變數，本國銀行、信用合作社各有6項，農會信用部有5項，漁會信用部有4項。 三、金融預警模型中，Logistic模型較Z-Score模型有較高的正確區別率。 / This research analyzes 49 domestic banks, 55 credit cooperative unions, 287 credit department of farmer associations and 27 credit department of fisherman associations above four kind of financial institution´s management situation, and refers the operation ways of various countries financial early warning system, selects suitable financial ratios , utilizes SAS statistics software and Z-score, Logistic models, it identifies the root cause of bankruptcy thus reveals finance of ratio the correlation, appraises management efficiency, the defeat probability each kind of financial institution if the correct difference rate. It appraises each kind of financial institution´s management efficiency, defeats probability and correct difference rate. It establishes early warning model that forecasts financial institutions failure rate. The research model and period: used 49 domestic banks from 2001 in 2nd season to the end of 2003 total 11 seasons and 25 items of finance ratio、55 credit cooperative associations from the end of 1998 to the end of 2003 total 21 seasons and 26 items of finance ratio、287 credit department of farmer associations and 27 credit department of fisherman associations from the end of 1998 to the end of 2003 total 21 seasons which used respectively 25 items of finance ratio. The conclusion of this research are: Firstly, it collects the entire Z-Score model to have significant financial indicator to each kind of financial institution, the domestic banks have 6 items, the credit cooperative associations have 7 items, the credit department of farmer associations have 6 items, and the credit department of fisherman associations have 4 items. Secondly, it collects the entire Logistic model to have significant financial indicator to each kind of financial institution, the domestic banks and the credit cooperative associations have 6 items respectively, the credit department of farmer associations have 5 items, and the credit department of fisherman associations have 4 items. Thirdly, in the financial early warning model, when comparing Z-Score with Logistic model , the latter appears to have a higher correct difference rate.