135,528 research outputs found
Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting
Forecasting how landslides will evolve over time or whether they will fail is
a challenging task due to a variety of factors, both internal and external.
Despite their considerable potential to address these challenges, deep learning
techniques lack interpretability, undermining the credibility of the forecasts
they produce. The recent development of transformer-based deep learning offers
untapped possibilities for forecasting landslides with unprecedented
interpretability and nonlinear feature learning capabilities. Here, we present
a deep learning pipeline that is capable of predicting landslide behavior
holistically, which employs a transformer-based network called LFIT to learn
complex nonlinear relationships from prior knowledge and multiple source data,
identifying the most relevant variables, and demonstrating a comprehensive
understanding of landslide evolution and temporal patterns. By integrating
prior knowledge, we provide improvement in holistic landslide forecasting,
enabling us to capture diverse responses to various influencing factors in
different local landslide areas. Using deformation observations as proxies for
measuring the kinetics of landslides, we validate our approach by training
models to forecast reservoir landslides in the Three Gorges Reservoir and
creeping landslides on the Tibetan Plateau. When prior knowledge is
incorporated, we show that interpretable landslide forecasting effectively
identifies influential factors across various landslides. It further elucidates
how local areas respond to these factors, making landslide behavior and trends
more interpretable and predictable. The findings from this study will
contribute to understanding landslide behavior in a new way and make the
proposed approach applicable to other complex disasters influenced by internal
and external factors in the future
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic
operation of power grids and energy markets. However, the non-linearity and
non-stationarity of electricity demand as well as its dependency on various
external factors renders STLF a challenging task. To that end, several deep
learning models have been proposed in the literature for STLF, reporting
promising results. In order to evaluate the accuracy of said models in
day-ahead forecasting settings, in this paper we focus on the national net
aggregated STLF of Portugal and conduct a comparative study considering a set
of indicative, well-established deep autoregressive models, namely multi-layer
perceptrons (MLP), long short-term memory networks (LSTM), neural basis
expansion coefficient analysis (N-BEATS), temporal convolutional networks
(TCN), and temporal fusion transformers (TFT). Moreover, we identify factors
that significantly affect the demand and investigate their impact on the
accuracy of each model. Our results suggest that N-BEATS consistently
outperforms the rest of the examined models. MLP follows, providing further
evidence towards the use of feed-forward networks over relatively more
sophisticated architectures. Finally, certain calendar and weather features
like the hour of the day and the temperature are identified as key accuracy
drivers, providing insights regarding the forecasting approach that should be
used per case.Comment: Keywords: Short-Term Load Forecasting, Deep Learning, Ensemble,
N-BEATS, Temporal Convolution, Forecasting Accurac
A Predictive Model with Data Scaling Methodologies for Forecasting Spare Parts Demand in Military Logistics
This study addresses the challenge of accurately forecasting demand for maintenance-related spare parts of the K-X tank, influenced by high uncertainty and external factors. Deep learning models with RobustScaler demonstrate significant improvements, achieving an accuracy of 86.90% compared to previous methods. RobustScaler outperforms other scaling models, enhancing machine learning performance across time series and data mining. By collecting eight years’ worth of demand data and utilising various consumption data items, this study develops accurate forecasting models that contribute to the advancement of spare parts demand forecasting. The results highlight the effectiveness of the proposed approach, showcasing its superiority in accuracy, precision, recall, and F1-Score. RobustScaler particularly excels in time series analysis, further emphasizing its potential for enhancing machine learning performance on diverse datasets. This study provides innovative techniques and insights, demonstrating the effectiveness of deep learning and data scaling methodologies in improving forecasting accuracy for maintenance spare parts demand
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