Modeling periphyton biomass in a flow-reduced river based on a least squares support vector machines model: Implications for managing the risk of nuisance periphyton
Modeling periphyton biomass in a flow-reduced river based on a least squares support vector machines model: Implications for managing the risk of nuisance periphyton
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基于最小二乘支持向量机模型对流量减少的河流中的附生生物生物量进行建模：对管理滋扰附生生物风险的影响
DOI：
10.1016/j.jclepro.2020.124884
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发表时间：
2021-03
影响因子：
11.1
通讯作者：
Jianmin Zhang
中科院分区：
文献类型：
--
作者：
Wei Huang;Leixiang Wu;Zhuowei Wang;Shirichiro Yano;Jiake Li;Gairui Hao;Jianmin Zhang
预测周围植物生物量对调控河流藻类生物量具有重要意义；然而，精确的建模仍然是一个挑战。许多传统的模型已经开发出来预测不同条件下的周围植物生物量，但它们仅在特定条件下有用，不能准确地模拟共同控制周围植物生物量的广泛因素。这促使我们考虑替代系统，包括在处理复杂系统中的非线性关系方面具有优势的人工智能方法。在本研究中，我们利用日本大山河的野外数据，基于三个成熟的模型计算了周围植物生物量。野外数据表明，平均叶绿素浓度为21.04 μg/cm2，超过了“有害”阈值。在三种模型中，最小二乘支持向量机（LS-SVM）模型的性能优于人工神经网络（ANN）和多元线性回归（MLR）模型。基于LS-SVM模型推导了水温、光照强度、总氮、总磷、流量与叶绿素的关系。确定了两种冲洗流量方案（即“退化”和“普通”条件下），并分别记录了10 m3/s和9 m3/s的最佳流速。研究结果可以改善河流管理，优化大坝运行，并减少河流下游流量减少的过度周生植物生长。
Predicting periphyton biomass is essential for controlling algal biomass in regulated rivers; however, accurate modeling remains a challenge. Many traditional models have been developed to predict periphyton biomass under different conditions, but they are only useful under specific conditions and cannot accurately model the wide range of factors that co-control the periphyton biomass. This prompts us to look at alternate systems, including artificial intelligence methods that have advantages in dealing with non-linear relationships in a complex system. In this study, we calculated the periphyton biomass based on three well-established models, using field data from the Ohyama River in Japan. The field data indicates that the average chlorophyllaconcentration was 21.04 μg/cm2, which is above the ‘nuisance’ threshold. Among the three models, the performance of the least squares-support vector machine (LS-SVM) model was found to be superior to those of the artificial neural network (ANN) and multiple linear regression (MLR) models. The relationships between water temperature, light intensity, TN, TP, discharge, and chlorophyllawere derived based on the LS-SVM model. Two scenarios for flushing flow were identified (i.e., for ‘degraded’ and ‘common’ conditions) and optimal flow rates of ∼10 m3/s and 9 m3/s, respectively, were recorded. The findings can improve river management, optimize dam operations, and reduce excess periphyton growth in flow-reduced downstream reaches of rivers.
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影响因子：
--
作者：
Biggs, BJF
通讯作者：
Biggs, BJF
DOI：
10.1111/j.1752-1688.2008.00265.x
发表时间：
2009-02
期刊：
JAWRA Journal of the American Water Resources Association
影响因子：
--
作者：
Michael W. Suplee;V. Watson;M. Teply;H. McKee
通讯作者：
Michael W. Suplee;V. Watson;M. Teply;H. McKee
影响因子：
2.7
作者：
M. Graba;S. Sauvage;N. Majdi;Benoît Mialet;F. Moulin;G. Urrea;E. Buffan‐Dubau;M. Tackx;S. Sabater;J. Sánchez-Pérez
通讯作者：
M. Graba;S. Sauvage;N. Majdi;Benoît Mialet;F. Moulin;G. Urrea;E. Buffan‐Dubau;M. Tackx;S. Sabater;J. Sánchez-Pérez
治理大型河流藻华的多因素识别和建模分析
影响因子：
8.9
作者：
Xia Rui;Zhang Yuan;Wang Gangsheng;Zhang Yongyong;Dou Ming;Hou Xikang;Qiao Yunfeng;Wang Qiang;Yang Zhongwen
通讯作者：
Yang Zhongwen
影响因子：
6.5
作者：
Vollmer, Derek;Regan, Helen M.;Andelman, Sandy J.
通讯作者：
Andelman, Sandy J.