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Found 11 papers, 2 papers with code
A Neural Emulator for Uncertainty Estimation of Fire Propagation
When compared to a related neural model (emulator) which was employed to generate probability maps via ensembles of emulated fires, the proposed approach produces competitive Jaccard similarity scores while being approximately an order of magnitude faster.
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning
For farmers and managers, the knowledge of when a picker bag is emptied is important for managing harvesting bins more effectively to minimise the time the picked fruit is left out in the heat (resulting in reduced shelf life).
An operational framework to automatically evaluate the quality of weather observations from third-party stations
With increasing number of crowdsourced private automatic weather stations (called TPAWS) established to fill the gap of official network and obtain local weather information for various purposes, the data quality is a major concern in promoting their usage.
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires
We show that popular optimisation cost functions used in the literature can result in PINNs that fail to maintain temporal continuity in modelled fire-fronts when there are extreme changes in exogenous forcing variables such as wind direction.
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution
We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks.
A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models
We propose a dedicated spatio-temporal neural network based framework for model emulation, able to capture the complex behaviour of fire spread models.
Deep Learning for Prawn Farming: Forecasting and Anomaly Detection
We present a decision support system for managing water quality in prawn ponds.
An Emulation Framework for Fire Front Spread
Empirical observations of bushfire spread can be used to estimate fire response under certain conditions.
Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction
We show that, though the neural network model achieves an accuracy of 80%, it requires long sequences to achieve this (100 samples or more).
Sequence-to-Sequence Imputation of Missing Sensor Data
Although the sequence-to-sequence (encoder-decoder) model is considered the state-of-the-art in deep learning sequence models, there is little research into using this model for recovering missing sensor data.
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature.
