Abstract

Biomass of the schooling fish Rastrineobola argentea (dagaa) is presently estimated in Lake Victoria by acoustic survey following the simple “rule” that dagaa is the source of most echo energy returned from the top third of the water column. Dagaa have, however, been caught in the bottom two-thirds, and other species occur towards the surface: a more robust discrimination technique is required. We explored the utility of a school-based random forest (RF) classifier applied to 120 kHz data from a lake-wide survey. Dagaa schools were first identified manually using expert opinion informed by fishing. These schools contained a lake-wide biomass of 0.68 million tonnes (MT). Only 43.4% of identified dagaa schools occurred in the top third of the water column, and 37.3% of all schools in the bottom two-thirds were classified as dagaa. School metrics (e.g. length, echo energy) for 49 081 manually classified dagaa and non-dagaa schools were used to build an RF school classifier. The best RF model had a classification test accuracy of 85.4%, driven largely by school length, and yielded a biomass of 0.71 MT, only c. 4% different from the manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time series.

中文翻译：

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使用随机森林对维多利亚湖声学调查数据中的银鲤类Rastrineobola argentea学校进行自动分类

摘要

鱼类Rastrineobola argentea的生物量根据简单的“规则”，dagaa是水柱顶部三分之一返回的大部分回波能量的来源，目前通过声学调查估算了维多利亚湖中的（dagaa）。但是，达加（Dagaa）被捕获在底部的三分之二处，其他物种也出现在水面：需要更强大的判别技术。我们探索了基于学校的随机森林（RF）分类器的实用性，该分类器适用于整个湖区调查中的120 kHz数据。达加（Dagaa）学校首先是使用钓鱼提供的专家意见手动确定的。这些学校的全湖生物量为68万吨（MT）。在已确定的dagaa学校中，只有43.4％出现在水域的顶部三分之一，而在底部三分之二的所有学校中，有37.3％被归为dagaa。学校指标（例如，时长，回声能量）用于49 081个手动分类的dagaa学校和非dagaa学校用于构建RF学校分类器。最好的射频模型的分类测试准确度为85.4％，这在很大程度上取决于学校的学习时间，并且产生的生物量为0.71 MT，仅为c。与人工估算有4％的差异。RF分类器提供了一种有效的方法来生成一致的dagaa生物量时间序列。