International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Review on Deep Learning Based Flood and Drought Prediction System |
|---|---|
| ABSTRACT | Floods and droughts are among the most severe natural disasters, significantly affecting the human life, agriculture, water resources, and economic development. With climate change the occurrence of such events has become more frequent and intense, making predictions with the traditional statistical and hydrological models more difficult. With the recent developments of Deep Learning (DL) technologies that offer powerful solutions for producing validated answers related to complex spatial and temporal data in the environment. This review provides an overview on deep learning (DL) technique including the convolutional neural network (CNN), the long short-term memory (LSTM), convLSTM, and the hybrid models for flood and drought forecast. It highlights the application of remote sensing, satellite information and climate data in enhancing the achievement of forecasting and the performance of forecasting. It covers the application of remote sensing, satellite data and climate data to enhance forecast skill. Some key research gaps were also identified, such as the lack of integration between flood and drought prediction, the lack of interpretability of models and the lack of real-time forecasting. The developed observations led to the formulation of hybrid CNN-LSTM network integrated into Explainable Artificial Intelligence (XAI) as potential frameworks for developing accurate, trustworthy, and understandable early warning systems for disaster management and climate adaptation. |
| AUTHOR | ADITYA NAGPURKAR, KRUTIKA THAKUR, AARUSHI GHADGE, YASH MULANKAR B. Tech Scholar, Department of Computer Engineering, St. Vincent Pallotti College of Engineering & Technology, Nagpur, Maharashtra, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407070 |
| pdf/70_A Review on Deep Learning Based Flood and Drought Prediction System.pdf | |
| KEYWORDS | |
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