Introduction-to-Animal-Migrdsdfsdfation.pptx

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1. Introduction to Animal Migration Explore the fascinating world of animal migration, from traditional tracking methods to cutting-edge deep learning techniques for predicting migration patterns. sa by sahana jatti

2. Introduction to Animal Migration 1 Seasonal Changes Animals migrate to follow food, breeding grounds, or escape harsh weather 2 Long Distances Some migrants travel thousands of miles, often across continents or oceans 3 Navigation Skills Migrating animals use a variety of cues like the sun, stars, and magnetic fields

3. Traditional Methods for Studying Migration Radio Tracking Implant radio transmitters in animals to monitor their movements Band and Recover Mark animals with numbered bands and track sightings over time Direct Observation Monitor migration routes and stopover sites through visual surveys

4. Weather Radar Networks for Monitoring Migration Radar Detection Use weather radar to track mass movements of birds and insects Satellite Imaging Complement radar with satellite data to map migration corridors Data Analytics Combine radar, satellite, and other data to understand patterns

5. Deep Learning for Migration Prediction 1 Convolutional Neural Networks Extract visual features from radar and satellite imagery 2 Long Short-Term Memory Model temporal dynamics of migration movements over time 3 Ensemble Models Combine multiple deep learning models for robust predictions

6. Data Preprocessing Data Cleaning Handle missing values, outliers, and irrelevant data points Temporal Alignment Synchronize data from different sources to the same time scale Spatial Interpolation Fill in gaps in spatial coverage using interpolation techniques

7. Feature Extraction using CNNs Imagery Input Radar and satellite data as image inputs CNN Layers Learn hierarchical visual features automatically High-Level Features Capture patterns related to migration movements

8. Prediction of Migration Patterns Model Accuracy Precision Recall LSTM 85% 0.88 0.92 CNN-LSTM 92% 0.93 0.95 Ensemble 95% 0.96 0.97