Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Sudhakara, B.Subject: search.filters.subject.satellite imagery

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    SOIL MOISTURE PREDICTION USING MULTI-SOURCE DATASETS AND GRAPH NEURAL NETWORK
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sudhakara, B.; Bhattacharjee, S.
    In this study, we introduce a novel method for predicting soil moisture (SM) in the Bidar district of Karnataka by leveraging a combination of advanced Machine Learning (ML) techniques and remote sensing data. The study utilizes multi-source inputs, such as MODIS satellite data, soil properties, elevation, and precipitation, to predict the SM. Our methodology implements state-of-the-art graph neural networks (GNNs), particularly Graph Convolutional Networks (GCNs), in combination with Long Short-Term Memory (LSTM) networks to capture both spatio-temporal dependencies in the data. The proposed model is compared with LSTM and CNN (Convolutional Neural Network)LSTM models and experimental results demonstrate that the GCN-LSTM model outperforms other approaches, achieving an R2 value of 0.9152, a mean absolute error (MAE) of 0.0240 m3/m3, and a root mean square error (RMSE) of 0.0322 m3/m3. These findings highlight the potential of graph neural networks in enhancing the accuracy of SM predictions, providing valuable insights for agricultural and water resource management in the region. © 2024 IEEE.
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    Drought Detection in India using Spatio-Temporal Graph Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Maheshwari, A.; Palakuru, S.; Bhattacharjee, S.
    Droughts, which are defined by extended periods of water scarcity, offer significant difficulties to agriculture, ecosystems, and human populations. Drought detection that requires timely and precise assessment is critical for effective mitigation and resource planning. This work proposes a novel technique for drought detection using satellite imagery with the capabilities of Graph Neural Networks (GNNs). The proposed GNN-based model captures spatio-temporal dependencies by representing 671 districts across India as nodes, connected based on geographical proximity. The spatio-temporal model achieved its best performance with an RMSE of 6.849, MAE of 4.367, and R2 of 0.903 for the Normalized Vegetation Supply Water Index (NVSWI). This work is one of the initial attempts to predict the drought over the Indian region using graph neural networks. © 2025 IEEE.