Dynamic Modeling of Water Level Changes Using Image Processing and Machine Learning

Background: Studying and monitoring the water level of rivers and canals by the Ministry of Energy and related organizations is an important part of water resources management in the catchment area.On the other hand, water level measurement is a vital task in hydrological monitoring, but it often faces limitations such as a lack of resources, high costs, and high time requirements.These limitations often lead to delays in measurements and potential inaccuracies, especially in remote or harsh environments.In general, most of the traditional methods have significant errors and costs and make continuous monitoring and control almost impossible.Recent advances in technology have led to a paradigm shift toward image-processing systems for water level monitoring.These non-contact methods have attracted attention due to their high potential in terms of accuracy, reliability, cost-effectiveness, and reduced time required.This research presents a new approach of combining image processing and machine learning in an attempt to reduce costs and increase performance to extract water level indicators and measure the water level instantly and automatically.This approach is based on creating a set of gauge images recorded by a smartphone (which is a common device with easy access) for clear and turbid water states to train machine learning-based models.Unlike the traditional methods with instantaneous and continuous measurement of the water level, this method makes water supply systems work better and manage critical situations such as floods, river overflows, and erosion.Methods: This paper contributes to this growing research by evaluating an image-based water level detection system using a standard smartphone camera.In this research, the RGB imageprocessing algorithms include filtering, noise reduction, color detection, resizing, grayscale conversion, Hough detection and transformation, and projection to obtain digital characters and watermarks that only include the area of scale lines.Moreover, all the mentioned steps and modeling steps have been done in Mathematica software.The experimental data include 244 observation data, which were randomly considered to be 201 training data images, and 43 test data images, which were captured by a mobile phone camera with a fixed position.Considering the capabilities of various machine learning models, including artificial neural networks (ANNs) and deep learning (DL) in image processing and analysis, this study focused on these models for accurate water level estimation.Our study involves taking water level images, identifying the water edge in a gauge, and using these models to estimate the water level.Machine learning, a branch of artificial intelligence, aims to develop computer systems with the capacity to learn from data.This process involves computer learning through hands-on experience, starting with organizing data, choosing a machine learning algorithm, entering data, and enabling the model to independently learn patterns or generate predictions, and gain self-programming capability.In general, the comparative analysis of the performance of these models aims to show the potential of combining image processing and machine learning in overcoming the traditional obstacles of water level measurement in hydrological studies. Results:The results of the model were evaluated using three evaluation criteria: coefficients of determination (R), root mean square error (RMSE), and Nash-Sutcliffe agreement coefficient (NSE), as well as visual charts.In this research, two ANNs and DL models, which are the subsets of machine learning models, were used to estimate the water level in muddy and clear conditions by image processing.The results showed that the DL model was acceptable in both conditions of performance.According to the evaluation indicators of the DL model with the lowest error of 28.39 mm and the highest R-value (0.973), it was chosen as the best model for water level estimation.Therefore, according to the performance of the DL model, it is possible to