Gupta Brij B, Gaurav Akshat, Attar Razaz Waheeb, Arya Varsha, Bansal Shavi, Alhomoud Ahmed, Chui Kwok Tai
Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan.
Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea.
Sci Rep. 2024 Dec 13;14(1):30459. doi: 10.1038/s41598-024-80099-6.
Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management. In the first layer of stacked models, Bi-directional LSTM is used to extract the features, and then in the second layer, the LSTM model will make the predictions. The model captures complex temporal dependencies by processing multivariate time series data in both forward and backward directions using bi-directional LSTM layers. Trained with the Mean Squared Error loss and Adam optimizer, the model demonstrates improved forecasting accuracy, offering significant potential for proactive drought management.
干旱是一种自然灾害，随着时间的推移，它会影响更大的区域。只有通过准确预测干旱，才能减少干旱造成的损失。在此背景下，我们提出了一种用于降雨预测的混合堆叠模型，这对于有效的干旱预测和管理至关重要。在堆叠模型的第一层中，使用双向长短期记忆网络（Bi-directional LSTM）来提取特征，然后在第二层中，长短期记忆网络（LSTM）模型将进行预测。该模型通过使用双向长短期记忆网络层在向前和向后两个方向上处理多变量时间序列数据来捕捉复杂的时间依赖性。该模型使用均方误差损失和亚当优化器进行训练，展示了提高的预测准确性，为主动的干旱管理提供了巨大潜力。
