Application of an artificial neural network formulated to predict the outbreak of musty odor and control it

应用人工神经网络预测霉味的爆发并加以控制

• 批准号: 12650543
• 负责人: HOSOMI Masaaki
• 金额: $ 0.9万
• 依托单位: Tokyo University of Agriculture and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2000
• 资助国家: 日本
• 起止时间: 2000 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12650543/
• 关键词: neural; network; model; musty odor; WATARASE RESERVDIR prediction; 2-mettly lisoborneol; 渡瀬遊水池

Outbreak of musty odor in reservoirs for drinking water caused serious problems with regard to water-supply such as musty taste of tap water. Control of musty odor is one of the most important tasks in water quality management of reservoirs. This paper describes the novel application of an artificial neural network (ANN) model based on the back-propagation method formulated to predict outbreak of a musty-odorous compound, 2-methylisoborneol (2-MIB), in Watarase Reservoir, which is one of drinking water resources for Tokyo area in Japan. By using ANN model, we constructed the model predicting absolute values of the 2-MIB concentrations in Watarase freshwater reservoir. As the input layer data, ANN model used various data obtained from 1992 to 1997, i.e. meteorological conditions, water qualities, nutrients, phytoplanktons, and operational conditions of Watarase Reservoir. Comparing the absolute values of 2-MIB concentrations calculated by the ANN model with those observed, it should be noted that the timing of outbreak of 2-MIB was well-predicted by the ANN model. Prediction of the absolute values of 3 days after- 2-MIB concentrations resulted in 0.65 of the correlation coefficient, thereby indicating good feasibility of predicting 2-MIB concentrations by the ANN model.

饮用水水库中发霉的气味爆发引起了供水的严重问题，例如自来水的霉味。控制霉味是水库水质管理中最重要的任务之一。本文介绍了基于后传播方法的人工神经网络（ANN）模型的新颖应用，该方法是为预测爆发的2-甲基异位酚（2-MIB）的爆发，在WATARASE储层中，这是日本东京地区的饮用水资源之一。通过使用ANN模型，我们构建了预测Watarase淡水储层中2-MIB浓度的绝对值的模​​型。作为输入层数据，ANN模型使用了从1992年到1997年获得的各种数据，即气象条件，水质，养分，浮游植物和Watarase储层的操作条件。将ANN模型计算出的2-MIB浓度的绝对值与观察到的那些浓度进行比较，应注意的是，2-MIB爆发的时机是由ANN模型很好地预测的。 2-MIB浓度后3天的绝对值预测导致相关系数为0.65，从而表明通过ANN模型预测2-MIB浓度的可行性良好。

项目成果

Hosomi, M., Tatemukai, H.: "Application of artificial neural network formulated to predict the outbreak of musty odor"Asian Waterqud 2001: First IWA Asia-Pacific Rogianal Conference, Fukuoka. Proceedings II. 187-192 (2001)

Hosomi, M., Tatemukai, H.：“应用人工神经网络来预测霉味的爆发”2001 年亚洲 Waterqud：第一届 IWA 亚太 Rogiinal 会议，福冈。

HOSOMI, M., I TATEMUKAI: "Application of an artificial neural network formulated to predict the outbreak of musty odor"ASIAN WATERQUAL 2001 First Asian-Pacific Regional Conference. Proceeding II. 187-192 (2001)

HOSOMI, M., I TATEMUKAI：“应用人工神经网络来预测霉味的爆发”2001 年亚洲水质量第一届亚太地区会议。

HOSOMI, M., TATEMUKAI, H.: "Application of an artificial neural network for mulated to predict the outbreak of musty odor"ASIAN WATERQUAL 2001 First IWA Asia-Pacific Regional Conference Proceedings II. 187-192 (2001)

HOSOMI, M., TATEMUKAI, H.：“应用人工神经网络模拟预测霉味的爆发”ASIAN WATERQUAL 2001 第一届 IWA 亚太地区会议论文集 II。