Modeling of Water Quality Parameters Using Data-Driven Models

Abstract

Water has a considerable role in all aspects of human life. Thus, evaluation of water characteristics in general and water quality in particular are necessary to enhance the health of humans and ecosystems. Data-driven models are computing methods that are capable of extracting different system states without using complex relationships. Prediction and simulation are two branches of data-driven modeling that use previous and previous-current data sets to fill gaps in time series. This paper investigates the capability of an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as two data-driven models to predict and simulate water quality parameters (e.g., sodium, potassium, magnesium, sulfates, chloride, pH, electrical conductivity, and total dissolved solids) at the Astane station in Sefidrood River, Iran. The writers considered six combinations of data sets, including the previously noted water quality parameters and river discharge in the previous and previous-current months, as input data. Implementation of the ANFIS and GP models in this paper illustrates the flexibility of GP in time series modeling relative to ANFIS, especially in the testing data set. Accordingly, the writers calculated the coefficient of variation of root mean squared error as the error criterion in different ANFIS and GP models (for assigning achievement probability to an appropriate solution) for each quality parameter. The average of the previously noted values for the six combinations of data sets improved (decreased) 80.51 and 80.89%, respectively, in the training and testing data sets with GP relative to ANFIS. These results indicate that the writers' proposed modeling, based on GP, is an effective tool for determining water quality parameters.

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

“…Therefore the researchers tend to rely on conceptual or empirical models in practical applications to reduce this uncertainty. A new modeling paradigm such as datadriven modeling or data mining has recently been a considerable growth in the development and application of computational intelligence and computer tools with respect to water-related problems [9,10].…”

A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

“…However, it does not mean such models cannot be used for environmental management. Actually, there is an increasing need of using data‐driven model for water quality management (e.g., Chang et al, 2015; Orouji et al, 2013; Ross & Stock, 2019; Shen et al, 2019). Shen et al (2019) shows a data‐driven model can be used for predicting the response of Chl‐a to changes in nutrient loading if the appropriate parameter variables are used.…”

Closure to “Application of a Hybrid Optimization Method in Muskingum Parameter Estimation” by Omid Bozorg Haddad, Farzan Hamedi, Elahe Fallah-Mehdipour, Hosein Orouji, and Miguel A. Mariño

“…In addition, they achieve global optimal solutions contingent on the specification of suitable initial parameter estimates, a nontrivial task (Geem 2011). This is one of the reasons for developing the evolutionary and metaheuristic algorithms (Ahmadi et al 2014;Ashofteh et al 2013aAshofteh et al , b, 2015aBeygi et al 2014;Bolouri-Yazdeli et al 2014;Bozorg-Haddad et al 2013Orouji et al 2013bOrouji et al , 2014Shokri et al 2013Shokri et al , 2014Seifollahi-Aghmiuni et al 2013;Soltanjalili et al 2013). On the other hand, the SFLA-NMS method is a hybrid algorithm of phenomenon-mimicking algorithms (SFLA) and mathematical techniques (NMS).…”