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ELE Master of Science Thesis Defense by Charles J. Berg

When: Wednesday, May 3, 2023
12:00 PM - 2:00 PM
Where: Science and Engineering Building 285 Old Westport Road, Dartmouth, MA
Cost: Free
Description: Topic: A Comparison of Two Methods for Acoustic Rainfall Estimation in Buzzards Bay

Location: Science & Engineering Building (SENG), Room 222

Zoom Conference Link:
Meeting ID: 916 8301 1749 Passcode: 646870

Ma and Nystuen (2005) successfully detected and estimated rainfall at sea from passive underwater acoustics. They detected rain from three narrowband frequencies (5.4, 8.3, and 21 kHz), and then estimated log rainfall rate via a regression with energy in the 5 kHz band. Mallary et al. (2022) improved rainfall detection by exploiting broadband spectra while reducing the dimensionality through principal component analysis (PCA). This project moves beyond detection to estimate the rainfall using the PCA-reduced acoustic power spectra. This defense compares two estimation methods: regression and quantization. Both estimation techniques start with the binary detection of rainfall using a Support Vector Machine (SVM) decision boundary (Boser et al., 1992). After binary detection, the regression estimates the rain rate on the rain detections using a Linear Minimum Mean Square Error regression trained on the ground truth rain rate against the PCA-reduced PSDs. The quantizer optimizes 5 rainfall quantization levels using Lloyds Algorithm (1982), then trains a multiclass classifier on the acoustic PSDs exploiting Dietterich and Bakiri's error-correcting output codes (1995) with multiple binary SVM classifiers. The quantizer uses this multiclass classifier to group the binary rain detections into one of 5 rain rate ranges and then estimates the rain rate by quantizing each range, or class, to the levels defined by Lloyds algorithm. These methods are compared using 43 days of acoustic and meteorological data collected on a mooring in Buzzards Bay, MA.

Advisor(s): Dr. John R. Buck, Chancellor Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

Committee Members: Dr. David A. Brown, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth;
Dr. Amit Tandon, Professor, Department of Mechanical Engineering, UMASS Dartmouth

NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.

*For further information, please contact Dr. John R. Buck at 508.999.9237 or via email at
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering