Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

Air temperature (AT) prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system (DENFIS), hybrid neural-fuzzy inference system (HyFIS), and adaptive neurofuzzy inference system (ANFIS) for AT prediction. Modelling was done for three stations in North Dakota (ND), USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly variable data, such as AT, which shows a high positive correlation with average daily dew point (DP), total solar radiation (TSR), and negative correlation with average wind speed (WS). At the Robinson station, DENFIS performed the best with a coefficient of determination (R$^{2}$) of 0.96 and a modified index of agreement (md) of 0.92, followed by ANFIS with R$^{2}$ of 0.94 and md of 0.89, and HyFIS with R$^{2}$ of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R$^{2}$: 0.953/0.960, md: 0.903/0.912, then ANFIS with R$^{2}$: 0.943/0.942, md: 0.888/0.890, and HyFIS with R$^{2}$ 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure

Operational satellite-based temporal modelling of Aedes population in Argentina

Aedes aegypti is a vector for Chikungunya, Dengue and Zika viruses in Latin America and is therefore a large public health problem for the region. For this reason, several inter-institutional and multidisciplinary efforts have been made to support vector control actions through the use of geospatial technologies. This study presents the development of an operational system for the application of free access to remotely sensed products capable of assessing the oviposition activity of Ae. aegypti in all of Argentina?s northern region with the specific aim to improve the current Argentine National Dengue risk system. Temporal modelling implemented includes remotely sensed variables like the normalized difference vegetation index, the normalized difference water index, day and night land surface temperature and precipitation data available from NASA?s tropical rainfall measuring mission and global precipitation measurement. As a training data set, four years of weekly mosquito oviposition data from four different cities in Argentina were used. A series of satellite-generated variables was built, downloading and resampling the these products both spatially and temporally. From an initial set of 41 variables chosen based on the correlation between these products and the oviposition series, a subset of 11 variables were preserved to develop temporal forecasting models of oviposition using a lineal multivariate method in the four cities. Subsequently, a general model was generated using data from the cities. Finally, in order to obtain a model that could be broadly used, an extrapolation method using the concept of environmental distance was developed. Although the system was oriented towards the surveillance of dengue fever, the methodology could also be applied to other relevant vector-borne diseases as well as other geographical regions in Latin America.Fil: Espinosa, Manuel. Fundación Mundo Sano; ArgentinaFil: Di Fino, Eliana Marina Alvarez. Fundación Mundo Sano; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Abril, Marcelo. Fundación Mundo Sano; ArgentinaFil: Lanfri, Mario. Centro Espacial Teófilo Tabanera; ArgentinaFil: Periago, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación Mundo Sano; ArgentinaFil: Scavuzzo, Carlos Marcelo. Centro Espacial Teófilo Tabanera; Argentin

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

A Model for early detection of potato late blight disease: a case Study in Nakuru County

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityThe agricultural sector has been a key backbone to Kenya’s economy. Agriculture has played a key role in the economy through agricultural farm produce exports and job creation hence improving and maintaining good farming practices is critical in ensuring agricultural yields. Potato (Solanum tuberosum L.) is a major food and cash crop for the country, widely grown by small-scale farmers in the Kenyan highlands. However, early detection of potato diseases such as potato late blight still remains a challenge for both farmers and agricultural extension officers.Consequently agricultural extension officers who play a critical role in training and creating awareness on sound agricultural practices are few and often lack sufficient knowledge and tools.Current techniques used for determining and detecting of crop diseases have heavily relied upon use human vision systems that try to examine physical and phenotypic characteristics such as leaf and stem color. This technique is indeed important for diagnosis of crop diseases, however the use of this technique is not efficient in supporting early detection of crop diseases. This study proposed use of sensors and back propagation algorithm for the prediction of potato late blight disease. Temperature and humidity sensor probes placed on the potato farms were instrumental in monitoring conditions for potato late blight disease. These parameters constituted abiotic factors that favor the development and growth of Phytophthora infestants. Back propagation neural network model was suitable for the prediction of potato late blight disease. In designing the potato late blight prediction model, historical weather data, potato variety tolerance on late blight disease was used to build an artificial neural network disease prediction model.Incoming data streams from the sensors was used to determine level and risk of blight. This study focused on a moderate susceptible cultivator of potato in developing the model. The algorithm was preferred due to its strengths in adaptive learning. The developed model achieved an accuracy of 93.89% while the precision obtained was 0.949. The recall ratio from the neural network was 0.968 and an F-measure of 0.964

Risk Management in Environment, Production and Economy

The term "risk" is very often associated with negative meanings. However, in most cases, many opportunities can present themselves to deal with the events and to develop new solutions which can convert a possible danger to an unforeseen, positive event. This book is a structured collection of papers dealing with the subject and stressing the importance of a relevant issue such as risk management. The aim is to present the problem in various fields of application of risk management theories, highlighting the approaches which can be found in literature

Innovation Issues in Water, Agriculture and Food

In a worldwide context of ever-growing competition for water and land, climate change, droughts and man-made water scarcity, and less-participatory water governance, agriculture faces the great challenge of producing enough food for a continually increasing population. In this line, this book provides a broad overview of innovation issues in the complex water–agriculture–food nexus, thus also relative to their interconnections and dependences. Issues refer to different spatial scales, from the field or the farm to the irrigation system or the river basin. Multidisciplinary approaches are used when analyzing the relationships between water, agriculture, and food security. The covered issues are quite diverse and include: innovation in crop evapotranspiration, crop coefficients and modeling; updates in research relative to crop water use and saving; irrigation scheduling and systems design; simulation models to support water and agricultural decisions; issues to cope with water scarcity and climate change; advances in water resource quality and sustainable uses; new tools for mapping and use of remote sensing information; and fostering a participative and inclusive governance of water for food security and population welfare. This book brings together a variety of contributions by leading international experts, professionals, and scholars in those diverse fields. It represents a major synthesis and state-of-the-art on various subjects, thus providing a valuable and updated resource for all researchers, professionals, policymakers, and post-graduate students interested in the complex world of the water–agriculture–food nexus

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Evaluation of agricultural land resources in Benin by regionalisation of the marginality index using satellite data

In the present work, the marginality index for agricultural land use was utilized to evaluate current and future biophysical resources for agricultural land use of Benin (West Africa) at a 1 km spatial resolution. The marginality index is an innovative capability evaluation approach that incorporates the main environmental factors, which limit agricultural production under low capital input. Furthermore, this index enables the detection of marginal sites, that is, sites prone to land degradation. In using this index, the feasibility of a global approach on a national scale was examined. Therefore, the same constraints, derived from input data at a higher spatial resolution, and adapted fuzzy logic based algorithms were used to determine the index for Benin. For the regionalisation, remote sensing data such as MODIS or SRTM were successfully applied to determine biophysical constraints. The outcome indicates that natural conditions are generally moderate suitable for agricultural land use in Benin, whereby most favoured regions are located in the south and centre of the country. Marginal sites can be found all over the country but in particular in northern regions. Currently, poor soils, limited length of growing period, and high rainfall variability are the crucial biophysical constraints on the national scale. Scenario analyses based on IPCC SRES scenarios A1B and B1 suggest that climate change will aggravate the natural suitability across Benin by 2025. Particularly temperature and the length of growing season will most likely impede future agricultural land use. In the context of this thesis, direct and indirect validation methods were conducted by applying GIS analyses and statistical tests. The direct methods are based on empirical knowledge and ground truth data recorded during field campaigns. For the indirect methods auxiliary data, namely disaggregated data of population density and trends of land degradation derived from NDVI data, were used. Both the direct and the indirect validation approach indicate the accuracy of the regionalisation outcome. Thus, the constraints considered herein on a global scale describing and defining marginal sites are, in an initial examination useful indicators on a national scale. Finally, based on biophysical constraints, population density, and trends of land degradation fields of investigations and corresponding location for national decision makers aiming a sustainable use of land resources were defined.Bewertung der agrarischen Ressourcen in Benin durch die Regionalisierung des Marginalitätsindexes mit Hilfe Satellitendaten In der vorliegenden Arbeit werden naturräumliche Ressourcen für eine landwirtschaftliche Nutzung in Benin (Westafrika) bewertet. Für die Bewertung wurde der Marginalitätsindex gewählt. Der Index ermöglicht die Identifizierung naturräumlich bedingter marginaler agrarischer Standorte sowie die Quantifizierung spezifischer Beschränkungsfaktoren. Damit stellt der Marginalitätsindex vor allem in Gebieten, wo traditionelle, wenig kapitalintensive, Anbaumethoden, weit verbreitet sind, ein interessante und innovative Möglichkeit dar, Landressourcen zu bewerten. Mit der Wahl des Marginalitätsindexes ist eine wesentliche Forschungsfrage dieser Arbeit verbunden: Kann der Ansatz, der auf globaler Ebene entwickelt wurde, auf die nationale Ebene übertragen werden? Um dieser Frage nachzugehen, wurde der Index aus räumlich höher aufgelösten Inputdaten und einem modifizierten Berechnungsalgorithmus für Benin in einer Auflösung von 1km x 1km berechnet. Fernerkundungsdaten, wie MODIS und SRTM-Datenprodukte, bieten dabei gute Möglichkeiten, aktuelle naturräumliche Beschränkungsfaktoren zu bestimmen. Das Ergebnis der Regionalisierung (MI) ermittelt für Benin durchschnittlich eine moderate naturräumliche Eignung für eine agrarische Nutzung. Gunstgebiete befinden sich überwiegend im Süden und Zentrum Benins. Marginale Flächen kommen dagegen landesweit vor, großflächig vor allem im Norden. Gegenwärtig bestimmt vor allem eine geringe Bodenfruchtbarkeit, zu kurze Vegetationsperioden und eine hohe Niederschlagsvariabilität die naturräumliche Gesamtmarginalität. Szenarienanalysen dieser Arbeit, basierend auf den IPCC SRES Klimaszenarien A1B und B1, deuten darauf hin, dass sich bis zum Jahr 2025 die naturräumlichen Produktionsgrundlagen deutlich verschlechtern werden. Insbesondere Temperaturanstieg und Verkürzungen der Anbauperiode bei gleichzeitig höherer Variabilität von Begin und Ende der Regenzeit werden landwirtschaftliche Aktivitäten erschweren. Zur Überprüfung der Ergebnisse von MI wurden direkte als auch indirekte Validierungsmethoden angewandt, die auf GIS-Analysen und statistischen Tests basieren. Die direkte Validierung bestand aus einem Vergleich mit eigenen Geländeaufnahmen sowie Überprüfung von Literaturangaben. Für die indirekte Validierung wurden zwei weitere Datensätze aufbereitet, die der Bevölkerungsdichte und Trends der gegenwärtigen Landdegradation. Ersteres wurde aus Zensusdaten disaggregiert und letzteres aus einer Zeitreihenanalyse unter Verwendung von NDVIDaten abgeleitet. Sowohl die direkte als auch die indirekte Validierung bestätigen das Ergebnis der Regionalisierung. Die gewählten globalen naturräumlichen Beschränkungsfaktoren entsprechen damit den wesentlichen Faktoren auf der nationalen Ebene. Eine nachhaltige Nutzung agrarischer Produktionsstandorte ist für die Gewährleistung der Ernährungssicherheit in stark landwirtschaftlich geprägten Ländern wie Benin von entscheidender Bedeutung. Aus diesem Grunde wurden auf der Basis der im Rahmen dieser Arbeit erzeugten Datensätze (MI, Bevölkerungsdichte und Trends der Landdegradation) zusätzlich Hauptinvestitionsfelder für eine nachhaltige Landnutzung ausgewiesen und eine entsprechende Karte erstellt.Evaluation des ressources agricoles au Bénin à l'aide d'une régionalisation de l'indice de marginalité utilisant les données satellitaires Dans le présent travail, l’indice de marginalité agricole des sols a été employé, avec une résolution de 1 km, pour évaluer les ressources biophysiques actuelles et futures dans le but d’une exploitation agricole des terres au Bénin (Afrique de l’Ouest). L'indice de marginalité agricole des sols est une approche intéressante et innovatrice d'évaluation des potentialités des sols. Son calcul fait intervenir les principaux facteurs environnementaux limitant la production agricole en cas de faibles apports en inputs agricoles. En outre, il permet l’identification et la localisation des sites marginaux, c’est-á-dire des sites susceptibles à la dégradation. En employant cet indice, la praticabilité d'une approche globale sur une échelle nationale a été examinée. Par conséquent, certains facteurs, dérivés des données de base d’une résolution spatiale plus élevée et les algorithmes de la logique floue adaptés ont été employés pour déterminer cet indice pour le Bénin. Pour la régionalisation, les données dérivées de la télédétection, notamment de MODIS ou SRTM, sont intéressantes et facilitent la détermination des contraintes biophysiques. Les résultats indiquent que les conditions naturelles pour la production agricole au Bénin sont généralement modérées, mais plus favorables au sud et au centre du pays. Les sites marginaux sont localisés dans tout le pays mais les grandes étendues marginales se trouvent au nord. Sur l’échelle nationale, les sols pauvres, la durée de la période de croissance végétative et la variabilité des précipitations constituent actuellement les contraintes biophysiques cruciales. Les analyses des scénarios A1B et B1 d'IPCC SRES montrent que d’ici 2025 le changement climatique détériora les aptitudes naturelles dans toutes les régions du Bénin. En particulier, la température et la durée de la saison de croissance des plantes entraveront l’exploitation agricole. Dans le contexte de cette thèse, des méthodes directes et indirectes de validation ont été effectuées en appliquant des analyses de SIG et des tests statistiques. Les méthodes directes sont basées sur la connaissance empirique et sur les données collectées sur terrain. Pour les méthodes indirectes, des données ont été auxiliairement employées, à savoir la densité démographique et les tendances de la dégradation des terres dérivées des données de NDVI. L’approche de validation directe et indirecte indique l'exactitude des résultats de régionalisation. Ainsi, les six contraintes décrivant et définissant les sites marginaux à l’échelle globale sont également applicables à l’échelle nationale. En conclusion, basé sur des contraintes biophysiques, la densité de la population et les tendances de la dégradation des terres, l’étude a permis de mettre en place un outil indispensable pour les décideurs nationaux visant une utilisation durable des terres ont été définies