1,304 research outputs found

    Flood dynamics derived from video remote sensing

    Get PDF
    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Étude de la rĂ©silience des milieux semi-arides Ă  agriculture familiale Ă  l’aide de donnĂ©es d’observation de la Terre

    Get PDF
    Les projections dĂ©mographiques selon les Nations-Unies, prĂ©voient un doublement de la population en Afrique Ă  partir de 2036, qui atteindra 20 % de la population mondiale en 2050. Cette situation crĂ©era davantage de pression pour satisfaire aux besoins de cette population grandissante. Dans ce contexte, la comprĂ©hension des interrelations complexes entre le climat, les activitĂ©s anthropiques et l’environnement bio-gĂ©ophysique devient essentielle pour minimiser les incertitudes liĂ©es aux changements climatiques, en particulier dans les rĂ©gions semi-arides vulnĂ©rables du continent. L’objectif principal de cette thĂšse est d’explorer la faisabilitĂ© de l’utilisation des donnĂ©es de tĂ©lĂ©dĂ©tection et des donnĂ©es auxiliaires multi-sources pour comprendre les vecteurs de la rĂ©silience des Ă©cosystĂšmes Ă©cologiques face aux perturbations des milieux semi-arides Ă  agriculture familiale en Afrique de l’Ouest (Mali et Burkina Faso). Le choix des sites d’étude intĂšgre les dimensions de la diversitĂ© Ă©cologique et climatique ainsi que les pratiques agroforestiĂšres dans cette rĂ©gion semi-aride. Dans ces sites, la comprĂ©hension des interrelations entre le climat, notamment les prĂ©cipitations et l’évolution de la vĂ©gĂ©tation naturelle a Ă©tĂ© Ă©tablie grĂące Ă  une analyse croisĂ©e entre une longue sĂ©rie temporelle du NDVI (AVHRR+MODIS) de 1984 Ă  2018 et une grille de prĂ©cipitation extraite de la base de donnĂ©es « Climate Hazards Group InfraRed Precipitation (CHIRP) » couvrant la mĂȘme pĂ©riode. L’étude part du principe que l’apprĂ©ciation de l’état de rĂ©silience des milieux concernĂ©s nĂ©cessite une combinaison des facteurs d’influence bio-gĂ©ophysiques (vĂ©gĂ©tation, Ă©rosion, emprise agricole) et des facteurs socioĂ©conomiques (niveau de vie). Les Ă©tats de la vĂ©gĂ©tation, de l’érosion et de l’emprise agricole ont Ă©tĂ© extraits des donnĂ©es d’observation de la Terre en utilisant des approches bien Ă©tablies. Un nouvel indice de pauvretĂ© multidimensionnelle adaptĂ© aux rĂ©gions tropicales semi-arides Ă  agriculture familiale a Ă©tĂ© proposĂ© dans le cadre de cette thĂšse. Son dĂ©veloppement a Ă©tĂ© soutenu par une enquĂȘte socioĂ©conomique basĂ©e sur 68 questions, et conduite auprĂšs de 1248 unitĂ©s de production agricole. Dans le cadre de cette enquĂȘte, une nouvelle stratĂ©gie de collecte de vĂ©ritĂ©s terrain a Ă©tĂ© proposĂ©e. Elle est basĂ©e sur trois sources diffĂ©rentes : autoĂ©valuation, Ă©valuation par les pairs et Ă©valuation par l’enquĂȘteur. Trois diffĂ©rents algorithmes d’intelligence artificielle ont Ă©tĂ© Ă©valuĂ©s afin de mettre au point l’indice de pauvretĂ© adaptĂ© au contexte des milieux semi-arides Ă  agriculture familiale. Il s’agit notamment de : RĂ©seaux de Neurones Artificiels (ANN), Support Vecteur Machine (SVM), et Random Forest (RF). L’indice proposĂ© est finalement basĂ© sur le RF, qui a donnĂ© les meilleurs rĂ©sultats de classification, avec des taux variant entre 78 % et 91 % pour les classes de niveau de vie. Par la suite, les principes de seuillage et d’attribution de scores qui dĂ©terminent les Ă©chelles de niveau de vie ont Ă©tĂ© appliquĂ©s dans un systĂšme d’information gĂ©ographique pour combiner les facteurs d’influence bio-gĂ©ophysiques avec l’indice de pauvretĂ© proposĂ©, dans le but de caractĂ©riser la rĂ©silience des terroirs villageois. Le nouvel indicateur rĂ©sultant de cette combinaison a Ă©tĂ© dĂ©signĂ© comme « Indice Multidimensionnel d’Équilibre du milieu (IME) ». L’apprĂ©ciation de la rĂ©silience des terroirs des villages a Ă©tĂ© faite suivant trois modalitĂ©s : rĂ©silient, vulnĂ©rable et dĂ©gradĂ©. Au Mali, les rĂ©sultats d’évaluation de la rĂ©silience montrent qu’aucun des terroirs villageois de la commune de Koury n’a le statut de rĂ©silient. En revanche, dans la commune de Sanekuy, deux terroirs villageois sont rĂ©silients. Dans le cas des communes concernĂ©es au Burkina Faso, la non-disponibilitĂ© des donnĂ©es de terrain impose une interprĂ©tation conditionnelle des rĂ©sultats. Ainsi, dans la commune de Boussouma, lorsque l’on considĂšre que tous les villages ont le statut de rĂ©silient au point de vue socioĂ©conomique, l’application de l’IME montre que 57% des villages se retrouvent dans un statut dĂ©gradĂ©. Lorsque l’on considĂšre que les conditions de vie de tous les villages sont dans le statut vulnĂ©rable, l’application de l’IME prĂ©sente un rĂ©sultat oĂč 74% des terroirs villageois sont dans un statut dĂ©gradĂ©. Dans la commune de Korsimoro, l’application de l’IME utilisant les diffĂ©rents statuts possibles de l’indice de pauvretĂ© multidimensionnelle adaptĂ©, prĂ©sente des rĂ©sultats Ă  dynamique similaire Ă  celle de Boussouma, oĂč, plus les conditions socioĂ©conomiques sont prĂ©caires, plus l’incidence est nĂ©gative sur le niveau de rĂ©silience de l’écosystĂšme Ă©cologique. Au regard de ces rĂ©sultats, l’application de l’IME montre que la rĂ©silience des Ă©cosystĂšmes Ă©cologiques ruraux est dynamique au rythme des pratiques agroforestiĂšres et des variations des prĂ©cipitations. Au-delĂ  de la possibilitĂ© de cartographier quantitativement et qualitativement l’état de rĂ©silience du milieu pour chaque facteur d’influence, cette Ă©tude innove par l’établissement d’un indice original d’équilibre du milieu permettant de caractĂ©riser la rĂ©silience des Ă©cosystĂšmes Ă©cologiques des zones tropicales semi-arides Ă  agriculture familiale.Abstract : Population projections, according to the United Nations, predict a doubling of the population in Africa by 2036, which will reach 20% of the world's population in 2050. This situation will lead to important pressure in order to satisfy the needs of this growing population. Understanding the complex interrelationships between climate, anthropogenic activities and the bio-geophysical environment is essential to minimize the uncertainties associated to climate change, especially in vulnerable semi-arid regions of African continent. The main objective of this thesis is to explore the feasibility of using remote sensing and multi-source data to understand the vectors of ecological ecosystems resilience in the semi-arid family farming environments in West Africa (Mali and Burkina Faso). The choice of the study sites takes into account the ecological dimension and climatic diversity of the tropical semi-arid areas. At these sites, an understanding of the interrelationships between climate and vegetation change, was established through a cross-analysis between a long term time series of the NDVI (AVHRR+MODIS) from 1984 to 2018 and, the precipitation grid extracted from the Climate Hazards Group InfraRed Precipitation (CHIRP) database covering the same period. The study assumes that the ecological ecosystem resilience assessment requires a combination of bio geophysical influencing factors (vegetation, erosion, agricultural footprint) and socio-economic factors (standard of living). The state of vegetation, erosion and land covering by agricultural were extracted from earth observation data using well-established approaches. A new multidimensional poverty index adapted to semi-arid tropical areas with family farming system was proposed as part of this thesis. A socio-economic survey involving 1248 agricultural production units based on 68 questions was conducted. A new strategy for collecting ground truth data was proposed based on three different sources (self-assessment, peer assessment and assessment by investigator). Three different algorithms were evaluated to develop a poverty index. These include Artificial Neural Networks (ANN); Support Vector Machine (SVM) and Random Forest (RF). The proposed index is ultimately based on the RF, which gave the best results of classification, with rates varying between 78% and 91% for the standard of living classes. Subsequently, the principles of thresholding and scoring were applied in a geographic information system (GIS) to combine bio geophysical influencing factors with the proposed poverty index, with the aim of characterizing the resilience status of target village’s areas. The new indicator resulting from this combination has been designated as the Multidimensional Middle Equilibrium Index (IME). Applied on Mali commune’s data, the ecological resilience assessment results show that none of the villages in Koury commune has the resilient status. On the other hand, in the commune of Sanekuy, 2 villages are resilient. In the case of Burkina Faso communes, the non-availability of data has conducted to a conditional interpretation of the results. Thus, in the commune of Boussouma, when we consider that all villages have the status of resilient from a socio-economic point of view, the application of the IME shows that 57% of villages find themselves in a degraded status. When we consider that the living conditions of all the villages are in the status of vulnerable, the application of the IME presents a result where 74% of the village are in a degraded status. In the commune of Korsimoro, the application of socio-economic resilience status scenarios shows the results with similar dynamics to Boussouma. Meaning that, the ecological ecosystems resilience of rural areas in semi-arid tropical zones is dynamic and linked to the agroforestry practices and to the rainfall variations. Beyond the possibility for mapping the state of ecological ecosystems resilience with regard to each influencing factor, this study innovates by establishing an original index of family farming environmental balance in the semi-arid tropical areas. In the process of the IME establishing, the study developed a new multidimensional poverty indicator, specifically adapted for semi-arid tropical areas with family farming, which is an innovation and an original contribution to science. Finally, a network learning approach of farmers interacting with agricultural research at the local level was experimented and conceptualized as an exploration of recommendations of methodological tools for adapting the agricultural production system to the ecological ecosystems resilience

    Undergraduate Catalog of Studies, 2022-2023

    Get PDF

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

    Get PDF
    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Digital agriculture: research, development and innovation in production chains.

    Get PDF
    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Discovering structure without labels

    Get PDF
    The scarcity of labels combined with an abundance of data makes unsupervised learning more attractive than ever. Without annotations, inductive biases must guide the identification of the most salient structure in the data. This thesis contributes to two aspects of unsupervised learning: clustering and dimensionality reduction. The thesis falls into two parts. In the first part, we introduce Mod Shift, a clustering method for point data that uses a distance-based notion of attraction and repulsion to determine the number of clusters and the assignment of points to clusters. It iteratively moves points towards crisp clusters like Mean Shift but also has close ties to the Multicut problem via its loss function. As a result, it connects signed graph partitioning to clustering in Euclidean space. The second part treats dimensionality reduction and, in particular, the prominent neighbor embedding methods UMAP and t-SNE. We analyze the details of UMAP's implementation and find its actual loss function. It differs drastically from the one usually stated. This discrepancy allows us to explain some typical artifacts in UMAP plots, such as the dataset size-dependent tendency to produce overly crisp substructures. Contrary to existing belief, we find that UMAP's high-dimensional similarities are not critical to its success. Based on UMAP's actual loss, we describe its precise connection to the other state-of-the-art visualization method, t-SNE. The key insight is a new, exact relation between the contrastive loss functions negative sampling, employed by UMAP, and noise-contrastive estimation, which has been used to approximate t-SNE. As a result, we explain that UMAP embeddings appear more compact than t-SNE plots due to increased attraction between neighbors. Varying the attraction strength further, we obtain a spectrum of neighbor embedding methods, encompassing both UMAP- and t-SNE-like versions as special cases. Moving from more attraction to more repulsion shifts the focus of the embedding from continuous, global to more discrete and local structure of the data. Finally, we emphasize the link between contrastive neighbor embeddings and self-supervised contrastive learning. We show that different flavors of contrastive losses can work for both of them with few noise samples

    Explainable Physics-informed Deep Learning for Rainfall-runoff Modeling and Uncertainty Assessment across the Continental United States

    Get PDF
    Hydrologic models provide a comprehensive tool to calibrate streamflow response to environmental variables. Various hydrologic modeling approaches, ranging from physically based to conceptual to entirely data-driven models, have been widely used for hydrologic simulation. During the recent years, however, Deep Learning (DL), a new generation of Machine Learning (ML), has transformed hydrologic simulation research to a new direction. DL methods have recently proposed for rainfall-runoff modeling that complement both distributed and conceptual hydrologic models, particularly in a catchment where data to support a process-based model is scared and limited. This dissertation investigated the applicability of two advanced probabilistic physics-informed DL algorithms, i.e., deep autoregressive network (DeepAR) and temporal fusion transformer (TFT), for daily rainfall-runoff modeling across the continental United States (CONUS). We benchmarked our proposed models against several physics-based hydrologic approaches such as the Sacramento Soil Moisture Accounting Model (SAC-SMA), Variable Infiltration Capacity (VIC), Framework for Understanding Structural Errors (FUSE), Hydrologiska ByrĂ„ns Vattenbalansavdelning (HBV), and the mesoscale hydrologic model (mHM). These benchmark models can be distinguished into two different groups. The first group are the models calibrated for each basin individually (e.g., SAC-SMA, VIC, FUSE2, mHM and HBV) while the second group, including our physics-informed approaches, is made up of the models that were regionally calibrated. Models in this group share one parameter set for all basins in the dataset. All the approaches were implemented and tested using Catchment Attributes and Meteorology for Large-sample Studies (CAMELS)\u27s Maurer datasets. We developed the TFT and DeepAR with two different configurations i.e., with (physics-informed model) and without (the original model) static attributes. Various catchment static and dynamic physical attributes were incorporated into the pipeline with various spatiotemporal variabilities to simulate how a drainage system responds to rainfall-runoff processes. To demonstrate how the model learned to differentiate between different rainfall–runoff behaviors across different catchments and to identify the dominant process, sensitivity and explainability analysis of modeling outcomes are also performed. Despite recent advancements, deep networks are perceived as being challenging to parameterize; thus, their simulation may propagate error and uncertainty in modeling. To address uncertainty, a quantile likelihood function was incorporated as the TFT loss function. The results suggest that the physics-informed TFT model was superior in predicting high and low flow fluctuations compared to the original TFT and DeepAR models (without static attributes) or even the physics-informed DeepAR. Physics-informed TFT model well recognized which static attributes more contributing to streamflow generation of each specific catchment considering its climate, topography, land cover, soil, and geological conditions. The interpretability and the ability of the physics-informed TFT model to assimilate the multisource of information and parameters make it a strong candidate for regional as well as continental-scale hydrologic simulations. It was noted that both physics-informed TFT and DeepAR were more successful in learning the intermediate flow and high flow regimes rather than the low flow regime. The advantage of the high flow can be attributed to learning a more generalizable mapping between static and dynamic attributes and runoff parameters. It seems both TFT and DeepAR may have enabled the learning of some true processes that are missing from both conceptual and physics-based models, possibly related to deep soil water storage (the layer where soil water is not sensitive to daily evapotranspiration), saturated hydraulic conductivity, and vegetation dynamics

    Digital agriculture: research, development and innovation in production chains.

    Get PDF
    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil.Translated by Beverly Victoria Young and Karl Stephan Mokross
    • 

    corecore