Multivariate Multi-Step Agrometeorological Forecast Model for Rapid Spray

Shi, Guobin; Chun, Wang

doi:10.1109/access.2021.3131649

Cited by 3 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lloret et al [32] proposed two deep learning models: dilated causal convolutional (DCCN) and encoder-decoder recurrent neural network (EDRNN), for forecasting disaggregated freight flows. Shi and Wang [33] proposed a Convolutional-LSTM encoder-decoder (ConvLSTM-AE) hybrid model for multivariate output and multi-step prediction with short time intervals to predict agrometeorological variables such as air temperature, relative humidity, and wind speed.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks

Hartomo,

Lopo,

Purnomo

2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

Background: Multi-output Time series forecasting is a complex problem that requires handling interdependencies and interactions between variables. Traditional statistical approaches and machine learning techniques often struggle to predict such scenarios accurately. Advanced techniques and model reconstruction are necessary to improve forecasting accuracy in complex scenarios. Objective: This study proposed an Encoder-Decoder network to address multi-output time series forecasting challenges by simultaneously predicting each output. This objective is to investigate the capabilities of the Encoder-Decoder architecture in handling multi-output time series forecasting tasks. Methods: This proposed model utilizes a 1-Dimensional Convolution Neural Network with Bidirectional Long Short-Term Memory, specifically in the encoder part. The encoder extracts time series features, incorporating a residual connection to produce a context representation used by the decoder. The decoder employs multiple unidirectional LSTM modules and Linear transformation layers to generate the outputs each time step. Each module is responsible for specific output and shares information and context along the outputs and steps. Results: The result demonstrates that the proposed model achieves lower error rates, as measured by MSE, RMSE, and MAE loss metrics, for all outputs and forecasting horizons. Notably, the 6-hour horizon achieves the highest accuracy across all outputs. Furthermore, the proposed model exhibits robustness in single-output forecast and transfer learning, showing adaptability to different tasks and datasets. Conclusion: The experiment findings highlight the successful multi-output forecasting capabilities of the proposed model in time series data, with consistently low error rates (MSE, RMSE, MAE). Surprisingly, the model also performs well in single-output forecasts, demonstrating its versatility. Therefore, the proposed model effectively various time series forecasting tasks, showing promise for practical applications. Keywords: Bidirectional Long Short-Term Memory, Convolutional Neural Network, Encoder-Decoder Networks, Multi-output forecasting, Multi-step forecasting, Time-series forecasting

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks

Hartomo,

Lopo,

Purnomo

2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

show abstract

Stock Market Prediction Using Multi-Objective Optimization

Zolfaghari

Fadishei

Tajgardan

et al. 2022

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)

View full text Add to dashboard Cite

Rainfall Forecasting with LSTM by Combining Cloud Image Feature Extraction with CNN and Weather Information

Sato,

Fujimoto

2024

IEEJ Journal IA

View full text Add to dashboard Cite

Concern about rainfall increase due to climate change and other factors is growing, inexpensive and easy-to-use rainfall forecasting methods are required. Therefore, this study developed a rainfall forecasting model using a neural network that uses readily available weather information such as cloud images, precipitation, and humidity. The proposed model achieved 89% accuracy for 24-hour-ahead classification, exceeding the 85% accuracy of the Japan Meteorological Agency.(JMA) In addition, by focusing on the seasonality of weather and introducing time information into the forecast model, the stability of the forecast was improved. Finally, a rainfall forecast model was developed and simulated by applying AdaBelief to EfficientNetV2+Bi-LSTM. Consequently, the accuracy of both 2-hour and 24-hour-forecasts exceeded the forecast precision of the previous study and the JMA. In particular, the 24-hour-ahead rainfall forecast precision was improved by more than 10% compared to the previous research, indicating a significant improvement in precision.

show abstract

Multivariate Multi-Step Agrometeorological Forecast Model for Rapid Spray

Cited by 3 publications

References 33 publications

Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks

Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks

Stock Market Prediction Using Multi-Objective Optimization

Rainfall Forecasting with LSTM by Combining Cloud Image Feature Extraction with CNN and Weather Information

Contact Info

Product

Resources

About