Earth and climate · Paper · June 2024
Navvye Anand, George Cheng, Tyler Rose
240 million people are affected by floods each year, reflecting the urgent need for accessible flood prediction and detection. WaterGate is a computational model that uses geographic elevation data and the rational method to predict flooding patterns, generating an interactive 3D model for user accessibility. Computational hydrology applies numerical methods, machine learning algorithms, and computational simulations to understand, predict, and manage water resources, including floods. Our project employs computational hydrology by analyzing the structure of river tributaries in 2D through polygon clustering, satellite imaging, and various cleaning protocols. We developed respective tributary tree graphs, morphological graphs, and nodes to create a comprehensive tree and 3D model. Afterward, we examine the morphology of flood plains in 3D space, implementing the rational method (Q = CiA) framework with curated relief plots to predict, model, and visualize flooding elevation. Then, we constructed our stream order analysis, waterline delineation, and statistical analysis to validate our data. Lastly, we modeled different river systems and developed further extensions to increase the applicability of WaterGate to communities around the world.
Earth and climate · Paper · June 2024
Aaryan Doshi
700 million people are in danger of being displaced due to inept drought prediction and prevention systems. Current research on drought assessment focuses solely on factors such as soil moisture and rainfall, which require painstaking measurements and lab samples, and can often be misleading. This research eliminates this requirement by proposing an end-to-end pipeline to detect and prevent droughts in at-risk areas using satellite images and vision transformers. The dataset is comprised of over 86,000 satellite images labeled by pastoralists and divided with an 80-20 ratio for training and validation. First, using feature filtering, normalization, and a Gaussian filter, the images in the dataset are modified to yield a better performance. Next, a deep vision transformer model with multi-headed attention is constructed, consisting of four heads, three transformer layers, and a patch size of five. The final MLP head produces logits for drought severity prediction level. Overall, the best transformer model achieves 78.3% accuracy in predicting drought conditions on a validation set of 10,000, unseen satellite images. In addition, this method outperforms state-of-the-art convolutional neural networks on this classification task, as compared to VGG-16, ResNet-50 and DenseNet-121 models. The model harnesses AWS cloud computing, deep vision transformers, and specific image augmentation to achieve state-of-the-art results in drought prediction and prevention. With this research, scientists have the potential to assess droughts quickly and accurately, revolutionizing our ability to provide resources and care to those affected by the increasingly common droughts caused by the climate crisis worldwide.
Earth and climate · Paper · June 2024
Advaith Anand
In the context of escalating climate change impacts, precise flood mapping has become crucial for effective disaster response and management. We address this challenge by developing a deep learning model based on the U-Net architecture, specifically tailored for rapid and accurate flood extent mapping using Synthetic Aperture Radar (SAR) data from the Copernicus Sentinel-1 mission published as part of IEEE 2024 GRSS data fusion challenge. Our approach leverages the unique capabilities of U-Net for detailed image segmentation, combined with SAR imagery, digital elevation models, land-use patterns, and historical water presence data to differentiate between water and non-water surfaces accurately. The model was rigorously trained and validated across a comprehensive dataset representing a variety of geographic conditions and flood events. It demonstrated significant advancements over traditional flood mapping techniques, achieving an overall accuracy of 95% and an F1-score of 0.7. These metrics underscore the model's effectiveness in accurately classifying flooded areas. Furthermore, the model exhibits excellent scalability and adaptability, offering potential applications beyond the initial flood mapping scope to other environmental and disaster management scenarios. Our findings not only contribute to the improvement of flood response strategies but also underscore the potential of advanced machine learning techniques in enhancing the accuracy and efficiency of environmental monitoring in the face of global climate challenges. This research represents a significant step forward in the application of deep learning to hydrological extremes, providing a robust tool for emergency response planners and climate scientists alike.
Earth and climate · Paper · June 2022
Christopher Sun
Since frequent severe droughts are lengthening the dry season in the Amazon Rainforest, it is important to detect wildfires promptly and forecast possible spread for effective suppression response. Current wildfire detection models are not versatile enough for the low-technology conditions of South American hot spots. This deep learning study first trains a Fully Convolutional Neural Network on Landsat 8 images of Ecuador and the Galapagos, using Green and Short-wave Infrared bands to predict pixel-level binary fire masks. This model achieves a 0.962 validation F2 score and a 0.932 F2 score on test data from Guyana and Suriname. Afterward, image segmentation is conducted on the Cirrus band using K-Means Clustering to simplify continuous pixel values into three discrete classes representing differing degrees of cirrus cloud contamination. Three additional Convolutional Neural Networks are trained to conduct a sensitivity analysis measuring the effect of simplified features on model accuracy and train time. The Experimental model trained on the segmented cirrus images provides a statistically significant decrease in train time compared to the Control model trained on raw cirrus images, without compromising binary accuracy. This proof of concept reveals that feature engineering can improve the performance of wildfire detection models by lowering computational expense.