Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020

Luft, Harrison; Schillaci, Calogero; Ceccherini, Guido; Vieira, Diana; Lipani, Aldo

doi:10.3390/fire5050163

Cited by 7 publications

(1 citation statement)

References 44 publications

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“…It turned out that the LSTM‐CNN model performed better than the CNN‐LSTM model, probably because using the LSTM layer as the starting layer allows each input unit to have an output unit with the memory/information of all the other units already processed. Afterwards, the one‐dimensional (1D) CNN layer receives the output, extracts the local features and makes predictions (Luft et al, 2022). However, in the CNN‐LSTM layer, the CNN layer, as the initial layer, reorganizes the data and extracts only some features (Lu et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

Wang

French²,

James³

et al. 2023

J of Sust Agri & Env

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BackgroundGrain commodities are important to people's daily lives and their fluctuations can cause instability for households. Accurate prediction of grain prices can improve food and social security.Methods & MaterialsThis study proposes a hybrid Long Short‐Term Memory (LSTM)‐Convolutional Neural Network (CNN) model to forecast weekly oat, corn, soybean and wheat prices in the United States market. The LSTM‐CNN is a multivariate model that uses weather data, macroeconomic data, commodities grain prices and snow factors, including Snow Water Equivalent (SWE), snowfall and snow depth, to make multistep ahead forecasts.ResultsOf all the features, the snow factor is used for the first time for commodity price forecasting. We used the LSTM‐CNN model to evaluate the 5, 10, 15 and 20 weeks ahead forecasting and this hybrid model had the lowest Mean Squared Error (MSE) at 5, 10 and 15 weeks ahead of prediction. In addition, Shapley values were calculated to analyse the feature contribution of the LSTM‐CNN model when forecasting the testing set. Based on the feature contribution, SWE ranked third, fifth and seventh in feature importance in the 5‐week ahead forecast for corn, oats and wheat, respectively, and 7–8 places higher than total precipitation, indicating the potential use of SWE in grain price forecasting.ConclusionThe hybrid multivariate LSTM‐CNN model outperformed other models and the newly involved climate data, SWE, showed the research potential of using snow as an input variable to predict grain prices over a multistep ahead time horizon.

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Section: Methodsmentioning

confidence: 99%