Super learner implementation in corrosion rate prediction

This thesis proposes a new machine learning model for predicting the corrosion rate of 3C steel in seawater. The corrosion rate of a material depends not just on the nature of the material but also on the material\u27s environmental conditions. The proposed machine learning model comes with a selection framework based on the hyperparameter optimization method and a performance evaluation metric to determine the models that qualify for further implementation in the proposed models’ ensembles architecture. The major aim of the selection framework is to select the least number of models that will fit efficiently (while already hyperparameter-optimized) into the architecture of the proposed model. Subsequently, the proposed predictive model is fitted on some portion of a dataset generated from an experiment on corrosion rate in five different seawater conditions. The remaining portion of this dataset is implemented in estimating the corrosion rate. Furthermore, the performance of the proposed models’ predictions was evaluated using three major performance evaluation metrics. These metrics were also used to evaluate the performance of two hyperparameter-optimized models (Smart Firefly Algorithm and Least Squares Support Vector Regression (SFA-LSSVR) and Support Vector Regression integrating Leave Out One Cross-Validation (SVR-LOOCV)) to facilitate their comparison with the proposed predictive model and its constituent models. The test results show that the proposed model performs slightly below the SFA-LSSVR model and above the SVR-LOOCV model by an RMSE score difference of 0.305 and RMSE score of 0.792. Despite its poor performance against the SFA-LSSVR model, the super learner model outperforms both hyperparameter-optimized models in the utilization of memory and computation time (graphically presented in this thesis)

Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data

A stochastic-geometric landsurface reflectance model is formulated and tested for the parameterization of spatially variable vegetation and soil at subpixel scales using satellite multispectral images without ground truth. Landscapes are conceptualized as 3-D Lambertian reflecting surfaces consisting of plant canopies, represented by solid geometric figures, superposed on a flat soil background. A computer simulation program is developed to investigate image characteristics at various spatial aggregations representative of satellite observational scales, or pixels. The evolution of the shape and structure of the red-infrared space, or scattergram, of typical semivegetated scenes is investigated by sequentially introducing model variables into the simulation. The analytical moments of the total pixel reflectance, including the mean, variance, spatial covariance, and cross-spectral covariance, are derived in terms of the moments of the individual fractional cover and reflectance components. The moments are applied to the solution of the inverse problem: The estimation of subpixel landscape properties on a pixel-by-pixel basis, given only one multispectral image and limited assumptions on the structure of the landscape. The landsurface reflectance model and inversion technique are tested using actual aerial radiometric data collected over regularly spaced pecan trees, and using both aerial and LANDSAT Thematic Mapper data obtained over discontinuous, randomly spaced conifer canopies in a natural forested watershed. Different amounts of solar backscattered diffuse radiation are assumed and the sensitivity of the estimated landsurface parameters to those amounts is examined

Besoin en eau et rendements des céréales en Méditerranée du Sud : observation, prévision saisonnière et impact du changement climatique

Le secteur agricole est l'un des piliers de l'économie marocaine. En plus de contribuer à 15% au Produit Intérieur Brut (PIB) et de fournir 35% des opportunités d'emploi, il a un impact sur les taux de croissance. Ces dernières sont affectées négativement ou positivement par les conditions climatiques et la pluviométrie en particulier. Lors des années de sécheresse, caractérisées par une baisse de la production agricole, en particulier celle des céréales, la croissance de l'économie marocaine a été sévèrement affectée et les importations alimentaires du royaume ont augmenté de manière significative. Dans ce contexte, il est important d'évaluer l'impact de la sécheresse agricole sur les rendements céréaliers et de développer des modèles de prévision précoce des rendements, ainsi que de déterminer l'impact futur du changement climatique sur le rendement du blé et leurs besoins en eau. Le but de ce travail est, premièrement, d'approfondir la compréhension de la relation entre le rendement des céréales et la sécheresse agricole au Maroc. Afin de détecter la sécheresse, nous avons utilisé des indices de sécheresse agricole provenant de différentes données satellitaires. En outre, nous avons utilisé les sorties du système d'assimilation des données terrestres (LDAS). Deuxièmement, nous avons développé des modèles empiriques de la prévision précoce des rendements des céréales à l'échelle provinciale. Pour atteindre cet objectif, nous avons construit des modèles de prévision en utilisant des données multi-sources comme prédicteurs, y compris des indices basés sur la télédétection, des données météorologiques et des indices climatiques régionaux. Pour construire ces modèles, nous nous sommes appuyés sur des algorithmes de machine learning tels que : Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) et eXtreme Gradient Boost (XGBoost). Enfin, nous avons évalué l'impact du changement climatique sur le rendement du blé et ses besoins en eau. Pour ce faire, nous nous sommes appuyés sur cinq modèles climatiques régionaux disponibles dans la base de données Med-CORDEX sous deux scénarios RCP4.5 et RCP8.5, ainsi que sur le modèle AquaCrop et nous nous sommes basés sur trois périodes, la période de référence 1991-2010, la deuxième période 2041-2060 et la troisième période 2081-2100. Les résultats ont montré qu'il y a une corrélation étroite entre le rendement des céréales et les indices de sécheresse liés à l'état de végétation pendant le stade d'épiaison (mars et avril) et qui sont liés à la température de surface pendant le stade de développement en janvier-février, et qui sont liés à l'humidité du sol pendant le stade d'émergence en novembre-décembre. Les résultats ont également montré que les sorties du LDAS sont capables de suivre avec précision la sécheresse agricole. En ce qui concerne la prévision du rendement, les résultats ont montré que la combinaison des données provenant de sources multiples a donné des meilleurs résultats que les modèles basés sur une seule source. Dans ce contexte, le modèle XGBoost a été capable de prévoir le rendement des céréales dès le mois de janvier (environ quatre mois avant la récolte) avec des métriques statistiques satisfaisants (R² = 0.88 et RMSE = 0.22 t. ha^-1). En ce qui concerne l'impact du changement climatique sur le rendement et les besoins en eau du blé, les résultats ont montré que l'augmentation de la température de l'air entraînera un raccourcissement du cycle de croissance du blé d'environ 50 jours. Les résultats ont également montré une diminution du rendement du blé jusqu'à 30% si l'augmentation du CO2 n'est pas prise en compte. Cependant, l'effet de la fertilisation au CO2 peut compenser les pertes du rendement, et ce dernier peut augmenter jusqu'à 27%. Finalement, les besoins en eau devraient diminuer de 13 à 42%, et cette diminution est associée à une modification de calendrier d'irrigation, le pic des besoins arrivant deux mois plus tôt que dans les conditions actuelles.The agricultural sector is one of the pillars of the Moroccan economy. In addition to contributing 15% in GDP and providing 35% of employment opportunities, it has an impact on growth rates that are negatively or positively affected by climatic conditions and rainfall in particular. During drought years characterized by a decline in agricultural production and in particular cereal production, the growth of the Moroccan economy was severely affected and the kingdom's food imports increased significantly. In this context, it's important to assess the impact of agricultural drought on cereal yields and to develop early yield prediction models, as well as to determine the future impact of climate change on wheat yield and water requirements. The aim of this work is, firstly to further understand the linkage between cereal yield and agricultural drought in Morocco. In order to identify this drought, we used agricultural drought indices from remotely sensed satellite data. In addition, we used the outputs of Land Data Assimilation System (LDAS). Secondly, to develop empirical models for early prediction of cereal yields at provincial scale. To achieve this goal, we built forecasting models using multi-source data as predictors, including remote sensing-based indices, weather data and regional climate indices. And to build these models, we relied on machine learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boost (XGBoost). Finally, to evaluate the impact of climate change on the wheat yield its water requirements. To do this, we relied on five regional climate models available in the Med-CORDEX database under two scenarios RCP4.5 and RCP8.5, as well as the AquaCrop model and we based on three periods, the reference period 1991-2010, the second period 2041-2060 and the third period 2081-2100. The results showed that there is a close correlation between cereals yield and drought indices related to canopy condition during the heading stage (March and April) and which are related to surface temperature during the development stage in January -February, and which are related to soil moisture during the emergence stage in November -December. The results also showed that the outputs of LDAS are able to accurately monitor agricultural drought. Concerning, cereal yield forecasting, the results showed that combining data from multiple sources outperformed models based on one data set only. In this context, the XGBoost was able to predict cereal yield as early as January (about four months before harvest) with satisfactory statistical metrics (R² = 0.88 and RMSE = 0.22 t. ha^-1). Regarding the impact of climate change on wheat yield and water requirements, the results showed that the increase in air temperature will result in a shortening of the wheat growth cycle by about 50 days. The results also showed a decrease in wheat yield up to 30% if the rising in CO2 was not taken into account. The effect of fertilizing of CO2 can offset the yield losses, and yield can increase up to 27 %. Finally, water requirements are expected to decrease by 13 to 42%, and this decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions

Development of Deep Learning Hybrid Models for Hydrological Predictions

The Abstract is currently unavailable, due to the thesis being under Embargo

Optimal Reservoir Design Criteria in Conjuctive use of Surface Water and Groundwater for Soybean Irrigation in Eastern Arkansas

A computer simulation model, named Arkansas Offstream Reservoir Analysis (ARORA) was developed to simulate present worth of net income from soybean production systems for conditions varying with respect to ground water availability, offstream reservoir capacity, and many other variables. Additional algorithms were incorporated into the model to enable it to optimize reservoir dimensions given realistic constraints and to identify the reservoir capacity corresponding to maximum present worth of simulated net income. The model was written in FORTRAN programming language and requires significant input data in order to provide significant flexibility with respect to the situations which may be accomodated. The model was demonstrated using 210 hypothetical situations which varied in terms of ground water availability, initial saturated depth of the aquifer, rate of decline of potentiometric surface, interest/discount rates, soil, and soybean price. The results were very reasonable and clearly point out that all of these variables impact optimal reservoir capacity, although no single variable is the sole determining factor in the decision of whether or not to construct a reservoir. The results further indicate that depending on model accuracy, there are many scenarios in which construction of a reservoir would be to the best interests of a soybean producer - especially those in regions with no ground water available or with a saturated aquifer depth of 25 ft or less

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Aeronautical Engineering: A continuing bibliography with indexes (supplement 166)

This bibliography lists 558 reports, articles and other documents introduced into the NASA scientific and technical information system in September 1983

Best bet options for Integrated Watershed Managemnet

Not AvailableWatershed is not simply the hydrological unit but also socio-political-ecological entity which plays crucial role in determining food, social, and economical security and provides life support services to rural people. The criteria for selecting watershed size also depend on the objectives of the development and terrain slope. A large watershed can be managed in plain valley areas or where forest or pasture development is the main objective. In hilly areas or where intensive agriculture development is planned, the size of watershed relatively preferred is small.Not Availabl

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Thermal Behaviour, Energy Efficiency in Buildings and Sustainable Construction

This Special Issue includes 20 contributions from across the world with very interesting and current research topics, such as insulation solutions and CO2 emissions; thermal transmittance of LSF walls; statistics for China’s building energy consumption; natural ventilation; thermal behavior of an earthbag building; thermal performance and comfort in a vernacular building; overheating risk under future extreme weather conditions; analytical methods to estimate the thermal transmittance of LSF walls; model simplification on energy and comfort simulation analysis; Trombe wall thermal behavior and energy efficiency of an LSF compartment; new metering hot box for in situ hygrothermal measurement; mechanical and thermal performance of compressed earth blocks; life-cycle assessment of a new house; energy analyses of Serbian buildings with horizontal overhangs; thermal properties of mortar blocks by using recycled glass; prediction of cooling energy consumption building using machine learning techniques; occupants’ behavior, climate change, heating, and cooling energy needs of buildings; a new method for establishing a hygrothermally controlled test room; nonintrusive measurements to incorporate the air renovations in dynamic models; and retrofit of existing buildings with aerogel panels