Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

Khadr, Mosaad; Elshemy, Mohamed

doi:10.1016/j.asej.2016.08.004

Cited by 57 publications

(23 citation statements)

References 30 publications

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“…Manzala Lake is located on the northeastern edge of the Nile Delta within three governorates; Dakahlia, Damietta and Port Said, and separated from the Mediterranean Sea by a sandy beach bridge [15]. It is located between latitudes 31°07`N and 31°30`N and longitudes 31°48`E and 32°17`E (Fig.…”

Section: Study Areamentioning

confidence: 99%

Physicochemical properties of Manzala Lake, Egypt

2017

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Section: Study Areamentioning

confidence: 99%

Physicochemical properties of Manzala Lake, Egypt

2017

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“…An adaptive neuro-fuzzy inference system (ANFIS) is a fuzzy inference system formulated with a learning algorithm [52]. Proposed by [53], ANFIS is based on the first-order Sugeno fuzzy model.…”

Section: Water Quantity and Quality Assessment Through Modelingmentioning

confidence: 99%

Can We Really Have It All?—Designing Multifunctionality with Sustainable Urban Drainage System Elements

et al. 2019

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Multifunctionality is seen as one of the key benefits delivered by sustainable urban drainage systems (SUDS). It has been promoted by both scientific research and practical guidelines. However, interrelations between different benefits are vaguely defined, thus highlighting a lack of knowledge on ways they could be promoted in the actual design process. In this research, multifunctionality has been studied with the help of scenario analysis. Three stormwater scenarios involving different range of SUDS elements have been designed for the case area of Kirstinpuisto in the city of Turku, Finland. Thereafter, the alternative design scenarios have been assessed with four criteria related to multifunctionality (water quantity, water quality, amenity, and biodiversity). The results showed that multifunctionality could be analyzed in the design phase itself, and thus provided knowingly. However, assessing amenity and biodiversity values is more complex and in addition, we still lack proper methods. As the four criteria have mutual interconnections, multifunctionality should be considered during the landscape architectural design, or else we could likely lose some benefits related to multifunctionality. This reinforces emerging understanding that an interdisciplinary approach is needed to combine ecological comprehension together with the system thinking into SUDS design, locating them not as individual elements or as a part of the treatment train, but in connection with wider social ecological framework of urban landscape.

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“…The efficacy of the models was assessed using several evaluation criteria. Results showed that the ANFIS models are suited and reliable for the water‐quality simulation …”

Section: Introductionmentioning

confidence: 99%

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

Fadaee

Mahdavi‐Meymand

Zounemat‐Kermani

2020

CLEAN Soil Air Water

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This study challenges the use of three nature-inspired algorithms as learning frameworks of the adaptive-neuro-fuzzy inference system (ANFIS) machine learning model for short-term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography-based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R 2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R 2 ), root-mean-square error (RMSE), Nash-Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all-nature-inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS-PSO and ANFIS-BOA provide slightly better results than the other ANFIS models.

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Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

Cited by 57 publications

References 30 publications

Physicochemical properties of Manzala Lake, Egypt

Physicochemical properties of Manzala Lake, Egypt

Can We Really Have It All?—Designing Multifunctionality with Sustainable Urban Drainage System Elements

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

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