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.

饮用水水库出​​现霉味，造成自来水有霉味等严重供水问题。霉味控制是水库水质管理的重要任务之一。本文描述了基于反向传播方法的人工神经网络 (ANN) 模型的新颖应用，该模型用于预测渡良濑水库中霉味化合物 2-甲基异龙脑 (2-MIB) 的爆发，该水库是日本东京地区的饮用水资源。通过使用ANN模型，我们构建了预测渡良濑淡水水库中2-MIB浓度绝对值的模​​型。 ANN模型使用1992年至1997年获得的各种数据作为输入层数据，即渡良濑水库的气象条件、水质、营养盐、浮游植物和运行条件。将 ANN 模型计算的 2-MIB 浓度绝对值与观测值进行比较，值得注意的是 ANN 模型很好地预测了 2-MIB 爆发的时间。预测3天后2-MIB浓度的绝对值导致相关系数为0.65，从而表明通过ANN模型预测2-MIB浓度具有良好的可行性。

项目成果

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。

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。