机译:使用神经网络和韩国的合奏树方法评估生化氧需求
Department of Railroad Construction and Safety Engineering Dongyang University Yeongju 36040 Republic of Korea;
Department of Civil Engineering Hamedan Branch Islamic Azad University Hamedan Iran;
Department of Water Engineering Shahid Bahonar University of Kerman Kerman Iran;
Department of Civil Engineering Ma State University Tbilisi Georgia;
Distinguished Professor and Caroline & William N. Lehrer Distinguished Chair in Water Engineering Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering Texas A&M University College Station TX 77843-2117 USA National Water Center UAE University Al Ain United Arab Emirates;
Biochemical oxygen demand; Deep echo state network; Extreme learning machine; Gradient boosting regression tree; Random forests; Water quality prediction;
机译:使用人工神经网络(ANN)通过生化需氧量和化学需氧量预测苏尔马河中的溶解氧
机译:使用人工神经网络(ANN)通过生化需氧量和化学需氧量预测苏尔马河中的溶解氧
机译:应用新型混合MLP-FFA方法预测生化需氧量和溶解氧的多层感知器(MLP)神经网络模型的不确定性评估:以兰加特河为例
机译:结合Kohonen网络模型和反向传播神经网络模型预测水平地下流人工湿地中污水资源的生化需氧量(BOD
机译:两种改进神经网络分类的新颖集成方法
机译:具有多种网络结构的进化集成神经网络池的家庭电力需求预测
机译:利用人工神经网络(aNNs)预测生化需氧量和化学需氧量对苏尔马河溶解氧的影响
机译:特拉普污水处理厂的一个未命名的La Trappe Creek支流的碳质生化需氧量(CBOD),氮生化需氧量(NBOD)和总磷(Tp)的最大日负荷总量