Monique Rainford, Linda A Barbour, Darlena Birch, Patrick Catalano, Ella Daniels, Caron Gremont, Nicole E Marshall, Kurt Wharton, Kent Thornburg
Barriers to implementing good nutrition in pregnancy and early childhood: Creating equitable national solutions
Annals of the New York Academy of Sciences
harmful algal blooms hab including red tides cyanobacteria significant environmental issue harmful effects aquatic ecosystems human health traditional methods detecting managing algal blooms limited reliance manual observation analysis timeconsuming costly recent advances machine learning ml technology shown promise improving accuracy efficiency algal bloom detection prediction paper provides overview latest developments using ml algal bloom detection prediction using various water quality parameters environmental factors first introduced ml algal bloom prediction using regression classification models explored imagebased ml algae detection utilizing satellite images surveillance cameras microscopic images study also highlights several realworld examples successful implementation ml algal bloom detection prediction examples show ml enhance accuracy efficiency detecting predicting algal blooms contributing protection aquatic ecosystems human health study also outlines recent efforts enhance field applicability ml models suggests future research directions recent interest explainable artificial intelligence xai discussed effort understand influencing environmental factors algal blooms xai facilitates interpretations ml model results thereby enhancing models usability decisionmaking field management improving overall applicability realworld settings also emphasize significance obtaining highquality fieldrepresentative data enhance efficiency ml applications effectiveness ml models detecting predicting algal blooms improved management strategies data quality pretreating missing data integrating diverse datasets unified database overall paper presents comprehensive review latest advancements managing algal blooms using ml technology proposes future research directions enhance utilization ml techniques