scholarly journals Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3328
Author(s):  
Lisa K. Gallagher ◽  
Jill M. Williams ◽  
Drew Lazzeri ◽  
Calla Chennault ◽  
Sebastien Jourdain ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydrological Processes ◽  
Educational Tool ◽  
Educational Strategies ◽  
Extreme Climatic Events ◽  
Educational Application ◽  
Significant Barrier ◽  
Technological Tool ◽  
Technical Challenges ◽  
Hydrologic Conditions

Hydrologists and water managers increasingly face challenges associated with extreme climatic events. At the same time, historic datasets for modeling contemporary and future hydrologic conditions are increasingly inadequate. Machine learning is one promising technological tool for navigating the challenges of understanding and managing contemporary hydrological systems. However, in addition to the technical challenges associated with effectively leveraging ML for understanding subsurface hydrological processes, practitioner skepticism and hesitancy surrounding ML presents a significant barrier to adoption of ML technologies among practitioners. In this paper, we discuss an educational application we have developed—Sandtank-ML—to be used as a training and educational tool aimed at building user confidence and supporting adoption of ML technologies among water managers. We argue that supporting the adoption of ML methods and technologies for subsurface hydrological investigations and management requires not only the development of robust technologic tools and approaches, but educational strategies and tools capable of building confidence among diverse users.

scholarly journals Denoising-based Image Compression for Connectomics

2021 ◽  
Author(s):  
David Minnen ◽  
Michał Januszewski ◽  
Alexander Shapson-Coe ◽  
Richard L. Schalek ◽  
Johannes Ballé ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Image Compression ◽  
Brain Tissue ◽  
Neural Circuits ◽  
Reconstruction Accuracy ◽  
Nanometer Resolution ◽  
Significant Barrier ◽  
The Cost

Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of reconstruction accuracy.

LSA in the Classroom

2012 ◽  
pp. 158-168
Author(s):  
Walter Kintsch ◽  
Eileen Kintsch
Keyword(s):  
Machine Learning ◽  
Middle School ◽  
Middle School Students ◽  
Semantic Similarity ◽  
Machine Learning Method ◽  
Learning Method ◽  
School Students ◽  
Educational Application

LSA is a machine learning method that constructs a map of meaning that permits one to calculate the semantic similarity between words and texts. We describe an educational application of LSA that provides immediate, individualized content feedback to middle school students writing summaries.

scholarly journals Virtual Reality in Biology: Can We Become Virtual Naturalists?

2021 ◽  
Author(s):  
Juliano Morimoto ◽  
Fleur Ponton
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Reality ◽  
Virtual Worlds ◽  
Mixed Reality ◽  
Biology Education ◽  
Educational Tool ◽  
Technological Advances ◽  
Biological Interest ◽  
The Future

Technological advances made Virtual and Mixed Reality (VMR) accessible at our fingertips. However, only recently VMR has been explored for the teaching of biology. Here, we highlight how VMR applications can be useful in biology education, discuss about caveats related to VMR use that can interfere with learning, and look into the future of VMR applications in the field. We then propose that the combination of VMR with Machine Learning and Artificial Intelligence can provide unprecedented ways to visualise how species evolve in self-sustained immersive virtual worlds, thereby transforming VMR from an educational tool to the centre of biological interest.

scholarly journals VIRTUAL LEARNING APPLYING EDUCATIONAL STRATEGIES IN TIMES OF COVID-19; CASE OF TECHNOLOGY IN MILITARY SCIENCES

2021 ◽  
Author(s):  
Flor Emperatriz Garcés Mancero ◽  
◽  
Magaly Margarita Narváez Ríos ◽  
Luis Germánico Gutiérrez Albán ◽  
Víctor Danilo Lazo Alvarado ◽  
...  
Keyword(s):  
Virtual Learning ◽  
Educational Process ◽  
Virtual Education ◽  
Educational Tool ◽  
Educational Strategies ◽  
Training School ◽  
Face To Face ◽  
Study Programs ◽  
The Face ◽  
Teaching Learning

The educational system in times of pandemic has had to transform itself urgently and unexpectedly to a virtual modality. This paper presents an exploratory study on the main difficulties encountered in the Soldiers Training School "Vencedores del Cenepa", where the objective of this work was to expose some strategies mediated by ICTs, for the virtualization of the teaching-learning process; When the didactic and functional methodology was applied in virtual education, I necessarily involve externalizing the demands of the teachings where they are enrolled; in this online educational process-COVID 19; the students of the institution consider their class grade as a basic educational tool, where the student himself, Virtual learning behaves as an extension of the face-to-face classroom, mainly supported by technologies that allow, even remotely, activities that challenge students to produce a collective text, electronic portfolio, infographic or video that address a topic, can be worked collaboratively. in particular related to the topic of the class, they are generally more accepted by students than exercises or questionnaires whose objective is to record the content of a discipline; Therefore, we must see the opportunity that shortens the distances and enriches the teachers' process, maintaining their structure and development of methods according to reality; where the development of study programs is allowed, strengthens relationships and instills collaboration among all actors.

scholarly journals Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

2019 ◽  
Author(s):  
Sina Faizollahzadeh ardabili ◽  
Amir Mosavi ◽  
Majid Dehghani ◽  
Annamária R. Várkonyi-Kóczy
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Model Performance ◽  
Hydrological Processes ◽  
Learning Methods ◽  
Earth Systems ◽  
Machine Learning Methods ◽  
Ensemble Techniques ◽  
Current State

Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

scholarly journals Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

2020 ◽  
Author(s):  
Yang Yang ◽  
Ting Fong May Chui
Keyword(s):  
Machine Learning ◽  
Management Practices ◽  
Hydrological Processes ◽  
Rainfall Time Series ◽  
Hydrograph Separation ◽  
Learning Methods ◽  
Drainage Systems ◽  
Factors Affecting ◽  
Machine Learning Methods ◽  
Hydrological Responses

Abstract. Sustainable drainage systems (SuDS) are decentralized stormwater management practices that mimic the natural drainage processes. Their modeling is often challenged by insufficient data and unknown factors affecting the hydrological processes. This study uses machine learning methods to model directly the correlation between hydrological responses and rainfalls at fine temporal scales in two catchments of different sizes. A feature engineering method is developed to extract useful information from rainfall time series and is used in combination with a nested cross-validation procedure to derive high-quality models and to estimate their generalization errors. The SHAP method is adopted to explain the basis of each prediction, which is then used for estimating catchment response time and hydrograph separation. The explanations of the predictions provide valuable insights into the models’ behavior and the involved hydrological processes. Thus, interpreting machine learning models is found as a useful way to study catchment hydrology.

scholarly journals Characterization of Hillslope Hydrologic Events Using Machine Learning Algorithms

2019 ◽  
Author(s):  
Eunhyung Lee ◽  
Sanghyun Kim
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Learning Algorithm ◽  
High Reliability ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Hydrological Processes ◽  
Machine Learning Algorithm ◽  
Self Organizing Map ◽  
Machine Learning Classification

Abstract. Time series of soil moisture were measured at 30 points for 396 rainfall events on a steep, forested hillslope between 2007 and 2016. We then analyzed the dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between weighting components of a self-organizing map (SOM). Generation patterns of two primary hillslope hydrological processes, namely, vertical flow and lateral flow, at the upslope and downslope areas were responsible for the distinction of the hydrologic events. Two-dimensional spatial weighting patterns in the SOM provided explanations for the relationships between rainfall characteristics and hydrological processes at different locations and depths. High reliability in hydrologic classification was achieved for both the driest and wettest events; as assessed through k-fold cross validation using 10 years of data. Representative soil moisture monitoring points were found through temporal stability analysis of the event structure delineated from the machine learning classification. Application of a supervised machine learning algorithm provided a scheme using soil moisture for the cluster identification of hydrologic event even without rainfall data which is useful to configure hillslope hydrologic process with the least cost in data acquisition.

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