{"title":"预测雨水生物过滤器去除粪便大肠菌群的新方法","authors":"S. Lai, C. Bu, R. Chin, X. Goh, F. Teo","doi":"10.31436/iiumej.v23i2.2173","DOIUrl":null,"url":null,"abstract":"Fecal coliform removal using stormwater biofilters is an important aspect of stormwater management. A model that can provide an accurate prediction of fecal coliform removal is essential. Therefore, feedforward backpropagation neural network (FBNN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using a range of input features, namely grass type, the thickness of biofilter, and initial concentration of E. coli, while the estimated final concentration of E. coli was the output variable. The ANFIS model shows a better overall performance than the FBNN model, as it has a higher R2-value of 0.9874, lower MAE and RMSE values of 3.854 and 6.004 respectively, and a smaller average percentage error of 14.2%. Hence, the proposed ANFIS model can be served as an advanced alternative to replace the need for laboratory work.\nABSTRAK: Penyingkiran kolifom tinja menggunakan turas biologi (bioturas) air hujan merupakan aspek penting dalam pengurusan air hujan. Model yang dapat menunjukkan anggaran tepat tentang penyingkiran kolifom tinja adalah penting. Oleh itu, model rangkaian suapan neural perambatan belakang (FBNN) dan sistem adaptasi inferen neuro-fuzi (ANFIS) telah dibentukkan menggunakan pelbagai ciri input, iaitu jenis rumput, ketebalan bioturas dan kepekatan awal E. coli, manakala anggaran kepekatan akhir bagi E. coli merupakan hasil pembolehubah. Model ANFIS menunjukkan peningkatan keseluruhan yang lebih baik berbanding model FBNN, kerana ia mempunyai nilai R2 yang lebih tinggi iaitu 0.9874, nilai MAE dan RMSE yang lebih rendah iaitu sebanyak 3.854 dan 6.004 masing-masing, dan ralat peratusan purata yang lebih kecil sebanyak 14.2%. Oleh itu, model ANFIS yang dicadangkan boleh dijadikan alternatif awal bagi menggantikan keperluan kerja makmal.","PeriodicalId":13439,"journal":{"name":"IIUM Engineering Journal","volume":"56 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application\",\"authors\":\"S. Lai, C. Bu, R. Chin, X. Goh, F. Teo\",\"doi\":\"10.31436/iiumej.v23i2.2173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fecal coliform removal using stormwater biofilters is an important aspect of stormwater management. A model that can provide an accurate prediction of fecal coliform removal is essential. Therefore, feedforward backpropagation neural network (FBNN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using a range of input features, namely grass type, the thickness of biofilter, and initial concentration of E. coli, while the estimated final concentration of E. coli was the output variable. The ANFIS model shows a better overall performance than the FBNN model, as it has a higher R2-value of 0.9874, lower MAE and RMSE values of 3.854 and 6.004 respectively, and a smaller average percentage error of 14.2%. Hence, the proposed ANFIS model can be served as an advanced alternative to replace the need for laboratory work.\\nABSTRAK: Penyingkiran kolifom tinja menggunakan turas biologi (bioturas) air hujan merupakan aspek penting dalam pengurusan air hujan. Model yang dapat menunjukkan anggaran tepat tentang penyingkiran kolifom tinja adalah penting. Oleh itu, model rangkaian suapan neural perambatan belakang (FBNN) dan sistem adaptasi inferen neuro-fuzi (ANFIS) telah dibentukkan menggunakan pelbagai ciri input, iaitu jenis rumput, ketebalan bioturas dan kepekatan awal E. coli, manakala anggaran kepekatan akhir bagi E. coli merupakan hasil pembolehubah. Model ANFIS menunjukkan peningkatan keseluruhan yang lebih baik berbanding model FBNN, kerana ia mempunyai nilai R2 yang lebih tinggi iaitu 0.9874, nilai MAE dan RMSE yang lebih rendah iaitu sebanyak 3.854 dan 6.004 masing-masing, dan ralat peratusan purata yang lebih kecil sebanyak 14.2%. 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引用次数: 1
摘要
使用雨水生物过滤器去除粪便大肠菌群是雨水管理的一个重要方面。一个能够提供粪便大肠菌群去除准确预测的模型是必不可少的。因此,前馈反向传播神经网络(FBNN)和自适应神经模糊推理系统(ANFIS)模型采用一系列输入特征,即草的类型、生物过滤器的厚度和大肠杆菌的初始浓度,而大肠杆菌的估计最终浓度作为输出变量。ANFIS模型的r2值较高,为0.9874,MAE和RMSE值较低,分别为3.854和6.004,平均百分比误差较小,为14.2%,整体性能优于FBNN模型。因此,提出的ANFIS模型可以作为一种先进的替代方案,以取代实验室工作的需要。摘要:Penyingkiran kolifom tija menggunakan turas biologi (bioturas) air hujan merupakan aspeting dalam pengurusan air hujan。模型yang dapat menunjukkan anggaran tepat tentang penyingkiran kolifom tinja adalah penting。Oleh itu,模型rangkaian suapan neural perambatan belakang (FBNN) dan system adaptasi inferen neuro-fuzi (ANFIS) telah dibentukkan menggunakan pelbagai ciri输入,iiitjenis输出,ketebalan bioturas dan kepekatan awal大肠杆菌,manakala anggaran kepekatan akhir bagi大肠杆菌merupakan hasil pembolehubah。模型ANFIS menunjukkan peningkatan keseluruhan yang lebih baik berbanding模型FBNN, kerana ia mempunyai nilai R2 yang lebih tinggi iiitu 0.9874, nilai MAE dan RMSE yang lebih rendah iitu sebanyak 3.854 dan 6.004 masing-masing, dan ralat peratusan purata yang lebih keecil sebanyak 14.2%。Oleh itu,模型ANFIS yang dicadangkan boleh dijadikan替代awal bagi menggantikan keperluan kerja makmal。
New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application
Fecal coliform removal using stormwater biofilters is an important aspect of stormwater management. A model that can provide an accurate prediction of fecal coliform removal is essential. Therefore, feedforward backpropagation neural network (FBNN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using a range of input features, namely grass type, the thickness of biofilter, and initial concentration of E. coli, while the estimated final concentration of E. coli was the output variable. The ANFIS model shows a better overall performance than the FBNN model, as it has a higher R2-value of 0.9874, lower MAE and RMSE values of 3.854 and 6.004 respectively, and a smaller average percentage error of 14.2%. Hence, the proposed ANFIS model can be served as an advanced alternative to replace the need for laboratory work.
ABSTRAK: Penyingkiran kolifom tinja menggunakan turas biologi (bioturas) air hujan merupakan aspek penting dalam pengurusan air hujan. Model yang dapat menunjukkan anggaran tepat tentang penyingkiran kolifom tinja adalah penting. Oleh itu, model rangkaian suapan neural perambatan belakang (FBNN) dan sistem adaptasi inferen neuro-fuzi (ANFIS) telah dibentukkan menggunakan pelbagai ciri input, iaitu jenis rumput, ketebalan bioturas dan kepekatan awal E. coli, manakala anggaran kepekatan akhir bagi E. coli merupakan hasil pembolehubah. Model ANFIS menunjukkan peningkatan keseluruhan yang lebih baik berbanding model FBNN, kerana ia mempunyai nilai R2 yang lebih tinggi iaitu 0.9874, nilai MAE dan RMSE yang lebih rendah iaitu sebanyak 3.854 dan 6.004 masing-masing, dan ralat peratusan purata yang lebih kecil sebanyak 14.2%. Oleh itu, model ANFIS yang dicadangkan boleh dijadikan alternatif awal bagi menggantikan keperluan kerja makmal.
期刊介绍:
The IIUM Engineering Journal, published biannually (June and December), is a peer-reviewed open-access journal of the Faculty of Engineering, International Islamic University Malaysia (IIUM). The IIUM Engineering Journal publishes original research findings as regular papers, review papers (by invitation). The Journal provides a platform for Engineers, Researchers, Academicians, and Practitioners who are highly motivated in contributing to the Engineering disciplines, and Applied Sciences. It also welcomes contributions that address solutions to the specific challenges of the developing world, and address science and technology issues from an Islamic and multidisciplinary perspective. Subject areas suitable for publication are as follows: -Chemical and Biotechnology Engineering -Civil and Environmental Engineering -Computer Science and Information Technology -Electrical, Computer, and Communications Engineering -Engineering Mathematics and Applied Science -Materials and Manufacturing Engineering -Mechanical and Aerospace Engineering -Mechatronics and Automation Engineering