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A study of using grey system theory and artificial neural network on the climbing ability of <i>Buergeria robusta</i>frog
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作者 Yuan-Hsiou Chang Tsai-Fu Chuang 《Open Journal of Ecology》 2013年第2期83-93,共11页
Ecological engineering is an emerging study of integrating both ecology and engineering, concerned with the design, monitoring, and construction of ecosystems. In recent years, the threat to amphibian animals is becom... Ecological engineering is an emerging study of integrating both ecology and engineering, concerned with the design, monitoring, and construction of ecosystems. In recent years, the threat to amphibian animals is becoming more and more serious. In particular, the loss of habitats caused by changes to the way land is used by human beings has hit amphibians particularly hard. Amphibians are known to be particularly vulnerable to human activities because they rely on both terrestrial and aquatic habitats for survival. With the increasing development of many areas in recent years, concrete structures are often installed along water bodies in order to increase the safety of local residents. The construction of concrete banks along rivers associated with human development has become a serious problem in Taiwan. Most ecosystems used by amphibians are lakes and stream banks, yet no related design solutions to accommodate the needs of amphibians. The need to develop the relevant design specification considering protecting the amphibian is imperative. Buergeria robusta, an endemic species in Taiwan, is tree frog widely distributed in lowland montane regions. Their breeding season is from April to September. They like to rest on trees or hide at caves during the daytime and move to the stream nearby in dusk for breeding. Males usually emit weak mating call while standing on stones. Sticky eggs are attached to undersides of rocks and stones. Tadpoles are found in slow flowing water of streams [1]. The goal of this study is to improve the understanding of the relationship between the climbing ability and the physical characteristics of amphibians. In this study, we use Artificial Neural Network to simulate the climbing ability of Buergeria robusta. Besides, Grey System Theory is also adopted to improve the performance of Artificial Neural Network. Artificial Neural Network (ANN) is a computing system that uses a large number of artificial neurons imitating natural neural ability to deal with an information network by computing system. The numerical results have show good agreement with the experimental results. The results can serve as a reference for technicians involved in future ecological engineering designs of banks throughout the world. 展开更多
关键词 ECOLOGICAL Engineering Artificial Neural Network GREY System Theory buergeria ROBUSTA
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应用MaxEnt模型预测海南溪树蛙地理分布 被引量:6
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作者 牟超盛 齐旭明 +5 位作者 谢林顺 钱天宇 周正彦 陆宇燕 莫燕妮 李丕鹏 《野生动物学报》 北大核心 2021年第3期809-816,共8页
海南溪树蛙(Buergeria oxycephala)是中国特有种,处于近危状态,仅分布于海南岛,预测海南溪树蛙潜在适生区可为物种保护与研究提供重要参考。依据该蛙68个物种位点数据和9个环境因子,利用最大熵模型(MaxEnt)与地理信息系统(GIS),对其在... 海南溪树蛙(Buergeria oxycephala)是中国特有种,处于近危状态,仅分布于海南岛,预测海南溪树蛙潜在适生区可为物种保护与研究提供重要参考。依据该蛙68个物种位点数据和9个环境因子,利用最大熵模型(MaxEnt)与地理信息系统(GIS),对其在海南岛的潜在适生区进行预测,并通过刀切法和单一环境因子的响应曲线分析其与环境因子之间的关系。结果表明:海拔、气温季节性变化、坡度和气温年较差这4个因子对模型增益效果最为明显。海南溪树蛙适生区的最佳环境因子范围,海拔430—1200 m;气温季节性变化32—35℃;坡度>5°和气温年较差5.5—16.0℃。通过受试者工作特征曲线(ROC)对模型精度进行验证的结果表明,ROC曲线下面积值为0.891,模型预测结果准确性良好。用natural breaks法将适宜空间分为非适生、低适生、中适生和高适生4个等级。海南溪树蛙适生区主要分布在海南岛中南山地和周边丘陵区域,东部沿海区域也有少量适生区。 展开更多
关键词 海南溪树蛙 适生区 环境因子 最大熵模型
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