Application of Deep Learning Neural Networks for Nitrate Prediction in the Klokot River, Bosnia and Herzegovina

机译：深度学习神经网络在Klokot河，波斯尼亚和黑塞哥维那硝酸盐预测中的应用

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Nitrate is one of the focal water quality indices in aquatic systems. However, proper estimation of nitrate concentration is a complicated task. In this article, capabilities of deep neural networks (DNN) and conventional artificial neural networks (ANN) to model and predict nitrate concentration in the Klokot River, Bosnia and Herzegovina were investigated. The measured data includes nitrate and pH values. Different scenarios were considered as the potential models for DNN and ANN structuring. The result showed DNN and ANN networks are incapable of precisely modeling the nitrate concentration in the Klokot River based on the designed scenarios. In addition, DNN was slightly superior to ANN in different terms of estimation accuracy.

机译：硝酸盐是水产系统中的焦水质指数之一。然而，正确估计硝酸盐浓度是复杂的任务。在本文中，研究了深度神经网络（DNN）和传统人工神经网络（ANN）的能力来进行模拟和预测KLOKOT河，博斯尼亚和黑塞哥维那硝酸盐浓度。测量数据包括硝酸盐和pH值。不同的情景被认为是DNN和ANN结构的潜在模型。结果显示DNN和ANN网络的基于设计的场景，不能精确地建模Klokot River中的硝酸盐浓度。此外，DNN以不同的估计精度略有优于安氏。

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《International Conference on Control, Instrumentation and Automation》|2021年|1-6|共6页
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Bahrudin Hrnjica; Ali Danandeh Mehr; Esad Jakupović; Aladin Crnkić; Redžo Hasanagić;
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Estimation; Artificial neural networks; Water quality; Predictive models; Data models; Rivers; Task analysis;

机译：估计;人工神经网络;水质;预测模型;数据模型;河流;任务分析;

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Application of Deep Learning Neural Networks for Nitrate Prediction in the Klokot River, Bosnia and Herzegovina

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