Development of a Statistical Emulator of US Crop Yields via a Deep Neural Network Approach

Agricultural systems are inherently complex; understanding these systems requires knowledge of climatology, plant physiology, soil physics, economics, and the human psychology of the farmers themselves. Decision support tools strive to leverage existing data to help guide stakeholders towards the best policies and practices for their situation. Quantitative crop simulation models are one decision support tool which can be used to predict crop yield with available data. Crop models can broadly be sorted into two categories: process-based models and statistical models. Process-based models simulate the underlying physiological mechanisms of crop growth and can potentially be applied in out-of-sample scenarios, however, running these models is highly computationally intensive. Statistical models are less computationally intensive but do not provide the same level of causal understanding and can be difficult to apply in out-of-sample scenarios. Statistical emulators retain some of the benefits of processed-based models while achieving the reduced computational speed of statistical models. This is accomplished by treating the results of a process-based model as “true” and training a statistical model on the corresponding inputs and outputs. The reduced computational requirements of statistical emulators make them well suited for integration with large-scale integrated assessment models. In this thesis a statistical emulator for crop yield in the contiguous United States is developed using a deep neural network (DNN) approach. Data from the Agricultural Model Intercomparison Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) datasets are used to train yield emulators for three crop models and three crops. The DNN model architecture was developed by combining two forms of neural networks, a fully connected neural network and a long short-term memory (LSTM) recurrent neural network, in an attempt to capture the different time scales of the relevant inputs. Data relating to soil characteristics, growing season, and weather aggregates are used as input to the DNN. Root mean square error (RMSE) was calculated for each model and crop combination by comparing the reported simulated yield from the GGCMI dataset and the emulated yield from the DNN statistical emulator. RMSE values for all but two emulators were below 10% of the simulated yield range. The normalized RMSE values are comparable with, RMSE values from statistical emulators of crop yield currently available in the literature. The RMSE values reported in this thesis can likely be improved with further optimization of the DNN model architecture and with tuning of relevant hyper-parameters. Given the growing volume of agricultural data and the growing complexity of process-based crop models, DNN approaches may be valuable for the development of future statistical emulators of crop yield as they allow for greater flexibility over current methods

Employing statistical model emulation as a surrogate for CFD

International audienceThis work focuses on substituting a computationally expensive simulator by a cheap emulator to enable studying applications where running the simulator is prohibitively expensive. The procedure consists of two steps. In a first step, the emulator is calibrated to closely mimic the simulator response for a number of pre-defined cases. In a second step the calibrated emulator is used as surrogate for the simulator in the otherwise prohibitively expensive application. An appealing feature of the proposed framework contrary to other approaches is that the uncertainty on the emulator prediction can be determined. While the proposed framework is applicable in virtually all areas of natural sciences, we discuss the approach and evaluate its performance based on a typical example in the realm of computational wind engineering, namely the determination of the wind field in an urban area

Inference in the context of uncertain complex urban environments for climate change conscious planning and design

This thesis looks at the urban environment as the centre of human habitation. It governs the comfort of much of the human population and is essential to life itself. In the modern world, it is governed at many levels and this thesis approaches two of them: modelling a building’s system and elements of urban city design. Urban climate, in the UK, is being increasingly affected by climate change and urban pollution remains a concern. How cities are maintained and designed is being adjusted to consider these interactions. This thesis looks at the impact of roughness of the cities-scape on wind speed, considered a factor capable of improving air quality . This thesis will looks at urban albedo and the impact it has on air temperature at ground level compared with the general degree of urban density. Uncertainty is a part of complex systems such as cities which contain many elements and in order to address this models are used to describe these system. A modeller will not have access to all information or the time to address every element at a high level of detail. The Gaussian processes used in this thesis have inherent uncertainty quantification, and they make estimates that make allowances for inaccuracies. This means conclusions drawn using this method can be considered more robust to uncertainties in the data. This thesis will examine empirical data using different methodologies to draw conclusions about model fitness of the methods used. The case studies that are used are the problem of emulator construction for the building energy models (BEMs) and two example relationships of urban weather from the Birmingham University Climate Laboratory (BUCL) and the urban fabric. Building energy use, through domestic, office and industrial consumption, is a major part of how we as a society consumes electricity/gas and this consumption is metered. Building systems are modelled using physical principles which requires a large amount of information about constructed systems, user behaviour and the ambient environment which is very costly justifying alternatives such as statistical modelling. This thesis will showcase how they can be used to address the issue of climate change for a building energy use, in cooling

Solar potential in early neighborhood design:a decision-support workflow based on predictive models

In light of the acknowledged need for a transition toward sustainable cities, neighborhoods and buildings, urban planners, architects and engineers have to comply with evermore demanding energy regulations. These decision-makers must be supported early-on in their process by adequate methods and tools. Indeed, early-design decisions, which concern parameters linked to the building form and urban layout, strongly dictate the solar exposure levels of buildings, in turn influencing their energy need (e.g. for heating and cooling) and production potential (e.g. through on-site active solar systems). Despite the spread of existing digital tools, limitations remain, withholding their integration into the early design process. These considerations lay down the context within which this doctoral research was carried out. The main objective of this thesis is the development of a performance-based workflow to support decision-making in early-design neighborhood projects. The performance is here defined through three criteria: (i) the daylight potential, quantified by the spatial daylight autonomy, (ii) the passive solar potential, quantified by the annual energy need for space heating and cooling, and (iii) the active solar potential, quantified by the annual energy production. The research process consisted of two main phases. First, the development of a performance assessment engine allowing real-time evaluation of an ensemble of buildings. Second, the integration of this method into a decision-support workflow, taking the form of a digital prototype that was tested among practitioners. For the first phase, a metamodeling approach was adopted to circumvent the limitations associated to simulations involving solving physics-based equations. Mathematical functions were obtained to predict the daylight and energy performance of a neighborhood, from a series of geometry- and irradiation-based parameters, easily computable at the early-design phase. To derive these functions (or metamodels), a neighborhood modeling and simulation procedure was executed to acquire a dataset of reference cases, from which the metamodels were trained and tested. The resulting multiple-linear regression functions, combined to an algorithm for quantifying the active solar potential from the irradiation data, formed our performance assessment engine. To assess its usability and relevance, the workflow was implemented as a prototype, supported by existing 3D modeling and scripting tools. Inspired by the emerging performance-driven and non-linear design paradigms, a multi-variant approach was adopted for this implementation; from the space of possible designs defined by a small set of user-inputs, a series of neighborhood variants are generated through a random sampling algorithm. Results of their evaluation by the core engine are displayed to allow a comparative assessment of the variants in terms of their morphology and solar potential. Having been tested among practitioners during workshops, the prototype appears promising for providing design decision-support. Direct feedback gathered from participants support the relevance of the approach and reveals multiple avenues for further improvement. Results collected during the workshops also allowed probing the validity boundaries of the metamodels: the prediction accuracy achieved attests the potential of the approach as an alternative to more complex methods, less adequate for exploring early-phase design alternatives