Machine learning to generate soil information

This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large data compilations. This compilation of these global datasets is severely limited by privacy concerns and, currently, no solution has been implemented to facilitate the process. If we consider the performance differences derived from the generality of global models versus the specificity of local models, there is still a debate on which approach is better. Either in global or local DSM, most applications are static. Even with the large soil datasets available to date, there is not enough soil data to perform a fully-empirical, space-time modelling. Considering these knowledge gaps, this thesis aims to introduce advanced ML algorithms and training techniques, specifically deep neural networks, for modelling large datasets at a global scale and provide new soil information. The research presented here has been successful at applying the latest advances in ML to improve upon some of the current approaches for soil modelling with large datasets. It has also created opportunities to utilise information, such as descriptive data, that has been generally disregarded. ML methods have been embraced by the soil community and their adoption is increasing. In the particular case of neural networks, their flexibility in terms of structure and training makes them a good candidate to improve on current soil modelling approaches

Modular development of manufacturing simulation models.

It is common practice within manufacturing companies to create simulation models at different time periods. These models are often used to represent various parts of the manufacturing systems. In general, these pre-built simulation models are required to be integrated together in order to evaluate the entire manufacturing system, this is not a simple task. This research addresses the issues involved in the integration of pre-built simulation models. An in depth literature review was carried out to identify current strategies to overcome these issues. Based on structured research work, a set of recommendations is proposed to ensure easy integration of models. This set of recommendations will help simulation practitioners to minimise the errors occurred during the integration of simulation models. The findings conclude more effort is required than is anticipated by most model builders and involves far more than 'just simply changing' the name of variables. A set of recommendations is therefore proposed to cope with the complexity and understanding of manufacturing systems. The research focuses on manufacturing systems but in general can be applied elsewhere

Modular development of manufacturing simulation models.

It is common practice within manufacturing companies to create simulation models at different time periods. These models are often used to represent various parts of the manufacturing systems. In general, these pre-built simulation models are required to be integrated together in order to evaluate the entire manufacturing system, this is not a simple task. This research addresses the issues involved in the integration of pre-built simulation models. An in depth literature review was carried out to identify current strategies to overcome these issues. Based on structured research work, a set of recommendations is proposed to ensure easy integration of models. This set of recommendations will help simulation practitioners to minimise the errors occurred during the integration of simulation models. The findings conclude more effort is required than is anticipated by most model builders and involves far more than 'just simply changing' the name of variables. A set of recommendations is therefore proposed to cope with the complexity and understanding of manufacturing systems. The research focuses on manufacturing systems but in general can be applied elsewhere

Modular development of manufacturing simulation models.

It is common practice within manufacturing companies to create simulation models at different time periods. These models are often used to represent various parts of the manufacturing systems. In general, these pre-built simulation models are required to be integrated together in order to evaluate the entire manufacturing system, this is not a simple task. This research addresses the issues involved in the integration of pre-built simulation models. An in depth literature review was carried out to identify current strategies to overcome these issues. Based on structured research work, a set of recommendations is proposed to ensure easy integration of models. This set of recommendations will help simulation practitioners to minimise the errors occurred during the integration of simulation models. The findings conclude more effort is required than is anticipated by most model builders and involves far more than 'just simply changing' the name of variables. A set of recommendations is therefore proposed to cope with the complexity and understanding of manufacturing systems. The research focuses on manufacturing systems but in general can be applied elsewhere

Development of a hydrologic community modeling system using a workflow engine

Community modeling is a comparatively new paradigm that emphasizes on developing evolving modeling systems through a collective effort. It has gained growing attention within the hydrologic communities because the demand of developing more holistic-view model systems addressing chemical, physical, and biological processes within the geo volumes of the hydrologic cycle. The development of a community modeling system involves a number of technical issues including how to seamlessly integrate various models/modules especially to mediate their communications and executions, how to improve development efficiency by migrating legacy codes, and how to improve model provenance and repeatability of model runs to name just a few. The major objective of our studies is to develop a hydrologic community modeling system (HCMS) that allows constructing seamlessly integrated, workflow-based hydrologic models with swappable and portable modules for retrieving data from various data sources, pre-processing, modeling, and post-analysis. The HCMS is built on the Microsoft’s TRIDENT workflowengine which assists in tackling many of the above technical issues during its development. Four libraries are incorporated into HCMS, i.e. a data retrieval, a dataprocessing, a hydrologic computation and a data analysis library, which support to access data from numerous online data repositories using SOAP/FTP protocols or from local data stores, transform source data into model inputs, perform hydrologic modeling, and analyze model results, respectively. It can potentially be applied to anywhere in the nation due to its access to data sets of nationwide coverage, and can reduce the workload of conducting hydrologic modeling tasks to a great level. Besides its feature of supporting parallel or concurrent executions as well as distributing computations in GRID environment can improve run-time efficiency. This thesis comprises three independent papers, which present the studies on (1) the current efforts that have been or are beingmade for community modeling, (2) the development of the HCMS using the Microsoft’s TRIDENT workflow engine, (3) the assessment on the applicability and performance of the TRIDENT-shelled HCMS by applying it to conduct hydrologic studies on the Schuylkill watershed located in the Southeastern Pennsylvania.Ph.D., Civil Engineering -- Drexel University, 201