Prediction of Water Quality in Riva River Watershed

Abstract

The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH4-N explained BOD and BOD, NH4-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH4-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH4-N.

Construction and Application of Reservoir Flood Control Operation Rules Using the Decision Tree Algorithm

“…The DT algorithm has the advantages of being easier to understand, being easier to implement, and requiring relatively less workload than other approaches. Therefore, it has been widely used to address water conservation problems such as flood forecasting [46,47], flood or drought risk assessment [48][49][50][51][52][53], flood or drought classification [54,55], water quality prediction [56,57], inter-basin water transfer dispatching [58], water level prediction [59,60], and hydropower station power generation dispatching [61]. Noymanee and Theeramunkong [46] adopted the boosted decision tree regression to forecast flood water levels in a real-time manner and achieved high forecasting accuracy.…”

Integrated Assessment of Surface Water Quality in Danube River Chilia Branch

“…Due to rapid changes in water quality in river ecosystems worldwide, caused by the impact of natural and anthropogenic factors on the catchment basin, it is of the utmost importance to implement appropriate programs and strategies for the efficient assessment of water quality, to support qualitative and quantitative decisions regarding environmental management and restoration of water resources and public health protection [1][2][3][4][5].…”

“…Depending on their chemical forms, nutrients, expressed in phosphorus (total and dissolved forms) and nitrogen (inorganic and total forms), are key water quality parameters for surface waters, entering the water body from point (municipal, industrial, and agricultural facilities) and diffuse (erosion and surface runoff, groundwater inflow, and atmospheric deposition) sources throughout the catchment area, with direct or indirect impacts on aquatic life, biomass growth, oxygen concentrations, water clarity, and sedimentation rates [1,2,6,7]. They play an important role in the eutrophication process and pose a serious problem for the monitoring and estimation of their effects on water quality in a riverine environment, being difficult to control [2,[8][9][10].…”

“…Sci. 2021, 11, x FOR PEER REVIEW 2 of 15 atmospheric deposition) sources throughout the catchment area, with direct or indirect impacts on aquatic life, biomass growth, oxygen concentrations, water clarity, and sedimentation rates [1,2,6,7]. They play an important role in the eutrophication process and pose a serious problem for the monitoring and estimation of their effects on water quality in a riverine environment, being difficult to control [2,[8][9][10].…”

Environmental Effect of Potential Radiopharmaceuticals Residuals

“…Knowing partition between n-octanol and water or logK ow is possible to predict the properties of organic compounds. logK ow has been used to develop quantitative structure-property relationships (QSPRs) for predicting the water solubility [23,24], toxicity [25,26], soil-water partitioning [27], sorption coefficients [28,29] and effects of organic compounds on the environment and human health. Combinations of these parameters in different computational models [30][31][32] provide reliable information about environmental behaviour of newly synthesis compound with a lack of experimental data.…”