Abstract
We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model. The ANN-based water quality prediction model was constructed by reflecting the actual meteorological observation data and the water quality factors classified using an exploratory factor analysis (EFA) for each unit watershed in Nam River. To apply spatial refinement of meteorological factors for each unit watershed, we used the data of the Sancheong meteorological station for Namgang A and B, and the data of the Jinju meteorological station for Namgang C, D, and E. The predicted water quality variables were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total phosphorus (T-P), and suspended solids (SS). The ANN evaluation results reveal that the Namgang E unit watershed has a higher model accuracy than the other unit watersheds. Furthermore, compared with Namgang C and D, Namgang E has a high correlation with water quality due to meteorological effects. The results of this study will help establish a water quality forecasting system based on probabilistic weather forecasting in the long term.
Highlights
Water supply demands are increasing with environmental changes in river watersheds and developments due to urbanization
A, which is located upstream from the Namgang Dam, water temperature (W.T), air temperature (T), T-N, and dissolved oxygen (DO) were classified as Factor 1 (F1), and discharge (Q) and SS as Factor 2 (F2)
For Namgang B, W.T, T, T-N, and DO were classified as F1, and chemical oxygen demand (COD), biochemical oxygen demand (BOD), total organic carbon (TOC), SS, and total phosphorus (T-P) as F2
Summary
Water supply demands are increasing with environmental changes in river watersheds and developments due to urbanization. River surface water is highly sensitive to climate change because it is exposed to sunlight and is directly affected by temperature Because these water quality factors have nonlinear relationships with meteorological factors such as rainwater and temperature, it is difficult to define the correlations between them. Ji [4] explained that great developments have been made in mathematical modeling for numerical simulation of water quality and that modeling is a powerful decision-making tool It takes a considerable amount of time and effort to develop a water quality prediction model that considers the complex environments of watersheds, including artificial factors in natural rivers and the physical characteristics of water quality factors. Active research on prediction models has been conducted using the data-based ANN model as well as a physics-based model to predict water quality variations
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