An Approach for Predicting River Water Quality Using Data Mining Technique

Abstract

Abstract. Water contains many chemical, physical, and biological impurities. Some impurities are benign while others are toxic. The quality of water is defined in terms of its physical, chemical, and biological parameters and ascertaining its quality is crucial before use for various intended purposes such as potable water, agricultural, industrial, etc. Various water analysis methods are employed to determine water quality parameters such as DO, COD, BOD, pH, TDS, salinity, chlorophyll-a, coli form, and organic contaminants such as pesticides. The list of potential water contaminants is exhaustive and impractical to test for in its entirety. Such water testing is sometimes costly and time consuming. This paper attempts to present application of data mining technique to build a model to predict a widely used gross water quality parameter called Biochemical oxygen demand (BOD). BOD is a measure of the amount of dissolved oxygen used by microbial oxidation of organic matter in wastewater. The standard method for measuring BOD is a 5-day process. Dilution of sample, constant pH and nutrient content besides the temperature of 20°C and dark area are required for correct results. High levels of nitrogen compounds yield false BOD results. Winkler titration which is also used to measure BOD is a chemical intensive process. Hence an automatic prediction model for BOD has been sought for accurate, cost-effective and time saving measurement. Based on data available for BOD measurements, this paper describes the development of a prediction model for BOD using a technique of data mining, namely, support vector machines (SVM). A correlation coefficient of 0.9471 and RMSE of 0.5019 was obtained for the BOD prediction model on river water quality data. The performance of the proposed model was also compared with two other models namely artificial neural network (ANN) and regression by discretization. Simulation results show that the proposed model performs better than the other two in terms of correlation coefficient and RMSE.

Estimación de la Demanda Bioquímica de Oxígeno (DBO5) en aguas residuales de las empresas de jeans de la ciudad de Pelileo utilizando redes neuronales artificiales

“…Por lo que el modelo neuronal no utiliza las ecuaciones que describen el proceso de la DBO5 en sus cálculos. En este tipo de modelo, las neuronas representan solo una parte de la función matemática que la red construye a partir del conjunto de observaciones (Baldiris et al, 2017;Gulyani et al, 2015).…”

Elaboración de un modelo neuronal artificial para la estimación de la demanda bioquímica de oxígeno en aguas marinas

“…Un R igual a 1, indica un ajuste perfecto entre los resultados del modelo y los valores de DBO observados. Mientras que un R igual a cero, o negativo describe una correlación nula entre la predicción y la observación de la DBO (Gulyani et al, 2015).…”

A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

“…where C is the complexity parameter that controls the trade-off between allowance and maximizing margin for misclassification [66]; ε i are positive real constants [67].…”

“…It was applied widely for training SVM especially for complex problems with large and complicated datasets [38]. During the SVM learning process, SMO is applied simultaneously to optimize the quadratic programming problems that has the penalty for misclassification, as shown in Equation (2) [66]. In other words, SMO is an algorithm that optimizes the result of the SVM classification algorithm.…”

“…Landslide model of SMOSVM was constructed using training dataset generated from the selected factors. Basically, selection of the complexity parameter (C > 0) affects performance of the SMOSVM model [66]. Therefore, the complexity parameter is needed to set up to obtain the highest predictive capability of the SMOSVM model.…”