Source: United States Geological Survey (USGS)

Training Set: 1973-01-01 to 2015-12-31

Test Set: 2016-01-01 to 2016-04-30

Seed Set: Last 30 Days

Temporal Resolution: 1 Day (24 Hours)

Spatial Resolution: 2 Degrees Latitude and 2 Degrees Longitude

Details: Earthquakes were reorganized into tensors, with each slice representing a single day and consisting of a matrix with the rows and columns representing approximate latitudes and longitudes. Each position in the matrix was filled with the highest magnitude earthquake occurring at that approximate location and time.

Notes: Due to the limited temporal and spatial resolution of the dataset, not all earthquakes from the original dataset were included in the final datasets.

Long Short Term Memory (LSTM) Recurrent Neural Network (RNN)

Hidden Layers: 3

Neurons Per Hidden Layer: 1024 nodes

Timesteps: 30 days

Epochs of Training: 66/100 (Trained to 100, but best performance achieved at 66)

Loss Function: Mean Squared Error

Library: Torch7

Predictive Accuracy and other statistical calculations are based on the following criteria:

- A True Positive is scored when the magnitude prediction is within plus or minus 1.0 of the actual earthquake event magnitude, and the actual earthquake event magnitude is greater than 0.0.
- A True Negative is scored when the magnitude prediction is within plus or minus 1.0 of the actual earthquake event magnitude, and the actual earthquake event magnitude is exactly 0.0.
- A False Positive is scored when the magnitude prediction is outside of plus or minus 1.0 of the actual earthquake event magnitude, and the actual earthquake event magnitude is exactly 0.0.
- A False Negative is scored when the magnitude prediction is outside of plus or minus 1.0 of the actual earthquake event magnitude, and the actual earthquake event magnitude is greater than 0.0.

True Positive (TP) = 4749

True Negative (TN) = 1930751

False Positive (FP) = 2133

False Negative (FN) = 6367

Total Population (TP + TN + FP + FN) = 1944000

Positive (TP + FN) = 11116

Negative (FP + TN) = 1932884

Predict Positive (TP) = 6882

Predict Negative (TP) = 1937118

Prevalence (PRE = positive / total_pop) = 0.0057181069958848 (0.57%)

True Positive Rate OR Sensitivity OR Recall (TPR = TP / positive) = 0.42722202231018 (42.72%)

False Negative Rate OR Miss Rate (FNR = FN / positive) = 0.57277797768982 (57.28%)

False Positive Rate OR Fall-Out (FPR = FP / negative) = 0.0011035323382055 (0.11%)

True Negative Rate OR Specificity (TNR = TN / negative) = 0.99889646766179 (99.89%)

Accuracy (ACC = (TP + TN) / total_pop) = 0.99562757201646 (99.56%)

Positive Predictive Value OR Precision (PPV = TP / predict_positive) = 0.69006102877071 (69.01%)

False Discovery Rate (FDR = FP / predict_positive) = 0.30993897122929 (30.99%)

False Omission Rate (FOR = FN / predict_negative) = 0.0032868415863153 (0.33%)

Negative Predictive Value (NPV = TN / predict_negative) = 0.99671315841368 (99.67%)

Positive Likelihood Ratio (PLR = TPR / FPR) = 387.14046477778

Negative Likelihood Ratio (NLR = FNR / TNR) = 0.57341075500103

Diagnostic Odds Ratio (DOR = PLR / NLR) = 675.15382542324