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Spatial-temporal prediction of algal bloom

conference contribution
posted on 2023-05-23, 18:39 authored by Shahriar, S, Rahman, A
We present an application of spatial-temporal prediction to track algal blooms. Algal bloom is an important water quality events in marine, coastal and estuarine environments. For a day, we first identify an area with anomalous algal growth represented by spatial points in the gridded data where values of Chlorophyll-a (indicator for algal bloom) are above a threshold chosen by domain scientists. To represent the shape of the algal bloom area, we create convex hull from spatial gridded points. We then find the radii from centroid of the convex hull. The radii are further used as features in predicting spatial region of the algal bloom using regression techniques. We also predict the centroid (represented in latitude and longitude) of an algal bloom area to track whether bloom area is moving. Experimental results show that our approach can reasonably predict algal bloom area and its centroid one day ahead using features from previous day. The prediction technique benefits towards decision support systems for aquaculture industry and environmental departments.

History

Publication title

Proceedings, ICNC 2013

Editors

H Wang, SY Yuen, L Wang, L Shao, X Wang

Pagination

973-977

ISBN

978-1-4673-4714-3

Department/School

School of Information and Communication Technology

Publisher

Curran Associates

Place of publication

Red Hook, New York, United States

Event title

2013 Ninth International Conference on Natural Computation (ICNC)

Event Venue

Shenyang, China

Date of Event (Start Date)

2013-07-23

Date of Event (End Date)

2013-07-25

Repository Status

  • Restricted

Socio-economic Objectives

Fisheries - aquaculture not elsewhere classified

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