Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach

JOURNAL OF CLEANER PRODUCTION

卷:271

Article

Rezaie-Balf, Mohammad¹  Attar, Nasrin Fathollahzadeh²  Mohammadzadeh, Ardashir³  Murti, Muhammad Ary⁴  Ahmed, Ali Najah⁵  Fai, Chow Ming⁶  Nabipour, Narjes⁷  Alaghmand, Sina⁸  El-Shafie, Ahmed^9,10 

[1] Grad Univ Adv Technol, Dept Water Engn, Kerman, Iran

[2] Urmia Univ, Water Engn Dept, Orumiyeh, Iran

[3] Univ Bonab, Dept Elect Engn, Fac Engn, Bonab, Iran

[4] Telkom Univ, Res Ctr IoT, Bandung 40257, Indonesia

[5] Univ Tenaga Nasl UNITEN, Inst Energy Infrastruct IEI, Kajang 43000, Selangor, Malaysia

[6] Univ Tenaga Nas, Inst Sustainable Energy ISE, Kajang 43000, Selangor, Malaysia

[7] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam

[8] Monash Univ, Dept Civil Engn, 23 Coll Walk, Clayton, Vic 3800, Australia

[9] Univ Malaya, Dept Civil Engn, Kuala Lumpur 50603, Malaysia

[10] United Arab Emirates Univ, Natl Water Ctr, Al Ain, U Arab Emirates

关键词: Physicochemical parameters;    Water quality index;    Data assimilation;    Ensemble Kalman filter;    Intrinsic time-scale decomposition;   

DOI  :  10.1016/j.jclepro.2020.122576

来源: Elsevier

【 摘 要 】

Water quality has a crucial impact on human health; therefore, water quality index modeling is one of the challenging issues in the water sector. The accurate prediction of water quality index is an essential requisite for water quality management, human health, public consumption, and domestic uses. A comprehensive review as an initial attempt is conducted on existing solutions through data-driven models. In addition, the ensemble Kalman filter is found to be a suitable data assimilation method, which is successfully applied in hydrological variables modeling and other complexes, nonlinear, and chaotic problems. In this study, a new application of ensemble Kalman filter-artificial neural network is proposed to predict water quality index using physicochemical parameters for two commonly pollutant rivers, namely Klang and Langat, in Malaysia. As a further attempt, in order to improve the models' performance, a new preprocessing technique is adopted as the newly constructed assimilated model. The results confirm that ensemble hybrid based intrinsic time-scale decomposition has reduced root mean square error by 24% for Klang and 34% for Langat, respectively, compared with the intrinsic time-scale decomposition-conventional neural network model. Overall, the developed assimilated methodology shows the robustness of the proposed ensemble hybrid model in analyzing water quality index over monthly horizons that experts could evaluate the water quality of rivers more efficiently. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free