A short review on the application of computational intelligence and machine learning in the bioenvironmental sciences

This paper aims to provide a short review on the application of computational intelligence (CI) and machine learning (ML) in the bioenvironmental sciences. To clearly illustrate the current status, we limit our focus to some key approaches, namely fuzzy systems (FSs), artificial neural networks (ANNs) and genetic algorithms (GAs) as well as some ML methods. The trends in the application studies are categorized based on the targets of the model such as animal, fish, plant, soil and water. We give an overview of specific topics in the bioenvironmental sciences on the basis of the review papers on model comparisons in the field. The summary of the modelling approaches with respect to their aim and potential application fields can promote the use of CI and ML in the bioenvironmental sciences

Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition

Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4–0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement

Artificial Intelligence in Landscape Architecture: A Survey of Theory, Culture, and Practice

This dissertation explores the role of artificial intelligence (AI) in shaping the landscape architecture profession. It looks at how AI has evolved in the field, its current influence, and its potential to change research, teaching, and professional practice. The research includes a detailed review of existing literature to identify trends in AI applications and gaps in knowledge. It also examines landscape architects\u27 attitudes towards AI, revealing a mix of enthusiasm for its benefits and concerns about its impact on creativity and design processes, and proposes new ways of thinking about and working with AI. The work brings a unique perspective on AI in the field and gives valuable insights for future research and practice

Graduate School of Engineering and Management Catalog 2018-2019

The Graduate Catalog represents the offerings, programs, and requirements in effect at the time of publication

Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004)

The invasion of freshwater ecosystems is a particularly alarming phenomenon in the Iberian Peninsula. Habitat suitability modelling is a proficient approach to extract knowledge about species ecology and to guide adequate management actions. Decision-trees are an interpretable modelling technique widely used in ecology, able to handle strongly nonlinear relationships with high order interactions and diverse variable types. Decision-trees recursively split the input space into two parts maximising child node homogeneity. This recursive partitioning is typically performed with axis-parallel splits in a top-down fashion. However, recent developments of the R packages oblique.tree, which allows the development of oblique split-based decision-trees, and evtree, which performs globally optimal searches with evolutionary algorithms to do so, seem to outperform the standard axis-parallel top-down algorithms; CART and C5.0. To evaluate their possible use in ecology, the two new partitioning algorithms were compared with the two well-known, standard axis-parallel algorithms. The entire process was performed in R by simultaneously tuning the decision-tree parameters and the variables subset with a genetic algorithm and modelling the presence-absence of the Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004), an invasive fish species that has spread across the Iberian Peninsula. The accuracy and complexity of the trees, the modelled patterns of mesohabitat selection and the variables importance were compared. None of the new R packages, namely oblique.tree and evtree, outperformed the C5.0 algorithm. They rendered almost the same decision-trees as the CART algorithm, although they were completely interpretable they performed from four to eight partitions in comparison with C5.0, which resulted in a more complex structure with 17 partitions. Oblique.tree proved to be affected by prevalence and it does not include the possibility of weighting the observations, which potentially discourage its actual use. Although the use of evtree did not suggest a major improvement compared with the remaining packages, it allowed the development of regression trees which may be informative for additional modelling tasks such as abundance estimation. Looking at the resulting decision-trees, the optimal habitats for the Iberian gudgeon were large pools in lowland river segments with depositional areas and aquatic vegetation present, which typically appeared in the form of scattered macrophytes clumps. Furthermore, Iberian gudgeon seem to avoid habitats characterised by scouring phenomena and limited vegetated cover availability. Accordingly, we can assume that river regulation and artificial impoundment would have favoured the spread of the Iberian gudgeon across the entire peninsula.The study has been partially funded by the national Research project IMPADAPT (CGL2013-48424-C2-1-R) with MINECO (Spanish Ministry of Economy) and Feder funds and by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment). This study was also supported in part by the University Research Administration Center of the Tokyo University of Agriculture and Technology. Finally, we are grateful to the colleagues who worked in the field data collection, especially Juan Diego Alcaraz-Henandez, Rui M. S. Costa and Aina Hernandez.Muñoz Mas, R.; Fukuda, S.; Vezza, P.; Martinez-Capel, F. (2016). Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004). Ecological Informatics. 34:22-34. https://doi.org/10.1016/j.ecoinf.2016.04.011S22343

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.</p

Air Force Institute of Technology Research Report 2004

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

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