Advance drought prediction through rainfall forecasting with hybrid deep learning model

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.

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Publication Details

Journal
Scientific Reports
Published
2024-12-13
DOI
https://doi.org/10.1038/s41598-024-80099-6
Citations
13
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
3.67

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article

Advance drought prediction through rainfall forecasting with hybrid deep learning model

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13 citations
Scientific Reports
Hydrology and Drought Analysis
3.67
article

Advance drought prediction through rainfall forecasting with hybrid deep learning model

Razaz Waheeb Attar, Akshat Gaurav, Brij B. Gupta, Kwok Tai Chui, Varsha Arya, Shavi Bansal, Ahmed Alhomoud
article en
13 citations

Abstract

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.

Scientific ReportsVol. 14(1)
Chandigarh University (IN), Princess Nourah bint Abdulrahman University (SA), Northern Border University (SA), Asia University (TW), Symbiosis International University (IN), Kyung Hee University (KR), Ronin Institute (US), University of Business and Technology (SA), University of Warsaw (PL), Lebanese American University (LB), University of Petroleum and Energy Studies (IN), Hong Kong Metropolitan University (HK)
Princess Nourah Bint Abdulrahman University, Northern Border University
Climate action
Openalex Percentile: Top 6%
Hydrology and Drought Analysis
3.67
DOIOpen Access PDFOpenAlex
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Advance drought prediction through rainfall forecasting with hybrid deep learning model — Razaz Waheeb Attar, Akshat Gaurav, et al. · Scientific Reports (2024) | TGRS Research Map | TGRS