Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data

This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artificial intelligence (AI) hub. The AdaBoost algorithm has excellent predictive performance and model suitability and was selected for random forest and gradient boosting (GB)-based boosting models. To predict the optimized WQ, we selected pH, SS, water temperature, total nitrogen(TN), dissolved total phosphorus(DTP), NH₃-N, chemical oxygen demand (COD), dissolved total nitrogen (DTN), and NO₃-N as the input variables of the AdaBoost model. Dissolved oxygen (DO) was used as the target variable. Third, an algorithm showing excellent predictive power was selected by analyzing the prediction accuracy according to the input variable by using the random forest or GB series algorithm in the initial model. Finally, the performance evaluation of the ultimately developed predictive model demonstrated that RMS was 0.015, MAE was 0.009, and R2 was 0.912. The coefficient of the variation of the root mean square error (CVRMSE) was 17.404. R2 0.912 and CVRMSE were 17.404, indicating that the predictive model developed meets the criteria of ASHRAE Guideline 14. It is imperative that government and administrative agencies have access to effective tools to assess WQ and pollution levels in their local bodies of water.

Section: Prior Researchmentioning

confidence: 99%

Urban River Dissolved Oxygen Prediction Model Using Machine Learning

Moon

Lee

et al. 2022

“…A new model combining DNN, data decomposition, and fuzzy clustering was proposed to predict water quality factors influencing algal blooms [28]. An SAE-DNN model, combining the stacked autoencoder (SAE) technique with DNN, was developed to estimate the concentration of phycocyanin in cyanobacteria [29]. In some regions, there is a shortage of data or data imbalance, and recent research is being conducted to address this issue.…”

Section: Introductionmentioning

confidence: 99%

Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms

Kim

et al. 2023

Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model to predict algae conditions and respond to algal blooms through the HAB alert system. This study aimed to establish an additional deep learning model to effectively respond to algal blooms. The prediction model is based on a deep neural network (DNN), which is a type of artificial neural network widely used for HAB prediction. By applying the synthetic minority over-sampling technique (SMOTE) to resolve the imbalance in the data, the DNN model showed improved performance during validation for predicting the number of cyanobacteria cells. The R-squared increased from 0.7 to 0.78, MAE decreased from 0.7 to 0.6, and RMSE decreased from 0.9 to 0.7, indicating an enhancement in the model’s performance. Furthermore, regarding the HAB alert levels, the R-squared increased from 0.18 to 0.79, MAE decreased from 0.2 to 0.1, and RMSE decreased from 0.3 to 0.2, indicating improved performance as well. According to the results, the constructed data-based model reasonably predicted algae conditions in the summer when algal bloom-induced damage occurs and accurately predicted the HAB alert levels for immediate decision-making. The main objective of this study was to develop a new technology for predicting and managing HABs in river environments, aiming for a sustainable future for the aquatic ecosystem.

“…Based on the different platforms of the observation sensor, hyperspectral remote sensing technology can be divided into airborne, spaceborne, hand-held, and groundbased. Airborne hyperspectral remote sensing is flexible in configuration and investigation time in spatial resolution, spectral range, number of bands, and bandwidth, etc., with high spectral and spatial resolution, but limited coverage, it is more suitable for small water bodies, and continuous monitoring cannot be realized due to flight restrictions [21][22][23]. Spaceborne hyperspectral coverage area is higher, but the spatial and spectral resolution is relatively rough, which is not suitable for small inland water bodies.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Cao

Sun

et al. 2021

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.