Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models

Twenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep learning-based model that can predict the concentrations of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). Findings revealed that the rain garden was capable of effectively reducing solids, organics, nutrients, and heavy metals from stormwater runoff during the five-year period when hydrologic and climate conditions have changed. Volume reduction was also high but can decrease over time due to the accumulation of solids in the facility which reduced the infiltration capacity and increased ponding and overflows especially during heavy rainfalls. A preliminary development of a water quality prediction model based on long short-term memory (LSTM) architecture was also developed to be able to potentially reduce the labor and costs associated with on-site monitoring in the future. The LSTM model predicted pollutant concentrations that are close to the actual values with a mean square error of 0.36 during calibration and a less than 10% difference from the measured values during validation. The study showed the potential of using deep learning architecture for the prediction of stormwater quality parameters entering rain gardens. While this study is still in the preliminary stage, it can potentially be improved for use in performance monitoring, decision-making regarding maintenance, and design of similar technologies in the future.

Download Full-text

Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System

Sensors ◽

10.3390/s21217271 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7271

Author(s):

Jian Zhou ◽

Jian Wang ◽

Yang Chen ◽

Xin Li ◽

Yong Xie

Keyword(s):

Water Quality ◽

Transfer Learning ◽

Prediction Accuracy ◽

Prediction Models ◽

Prediction Method ◽

Water Area ◽

Quality Information ◽

Quality Prediction ◽

Monitoring Point ◽

Water Quality Prediction

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.

Download Full-text

Efficient water quality prediction models based on machine learning algorithms for Nainital Lake, Uttarakhand

Materials Today Proceedings ◽

10.1016/j.matpr.2021.12.334 ◽

2022 ◽

Author(s):

Manisha Koranga ◽

Pushpa Pant ◽

Tarun Kumar ◽

Durgesh Pant ◽

Ashutosh Kumar Bhatt ◽

...

Keyword(s):

Machine Learning ◽

Water Quality ◽

Prediction Models ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Quality Prediction ◽

Water Quality Prediction

Download Full-text

FM-GRU: A Time Series Prediction Method for Water Quality Based on seq2seq Framework

Water ◽

10.3390/w13081031 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1031

Author(s):

Jianlong Xu ◽

Kun Wang ◽

Che Lin ◽

Lianghong Xiao ◽

Xingshan Huang ◽

...

Keyword(s):

Water Quality ◽

Prediction Models ◽

Time Series Prediction ◽

Prediction Method ◽

Quality Data ◽

Enterprise Management ◽

Feature Interaction ◽

Quality Prediction ◽

Water Quality Data ◽

Water Quality Prediction

Water quality prediction plays a crucial role in both enterprise management and government environmental management. However, due to the variety in water quality data, inconsistent frequency of data acquisition, inconsistency in data organization, and volatility and sparsity of data, predicting water quality accurately and efficiently has become a key problem. This paper presents a recurrent neural network water quality prediction method based on a sequence-to-sequence (seq2seq) framework. The gate recurrent unit (GRU) model is used as an encoder and decoder, and a factorization machine (FM) is integrated into the model to solve the problem of high sparsity and high dimensional feature interaction in the data, which was not addressed by the water quality prediction models in prior research. Moreover, due to the long period and timespan of water quality data, we add a dual attention mechanism to the seq2seq framework to address memory failures in deep learning. We conducted a series of experiments, and the results show that our proposed method is more accurate than several typical water quality prediction methods.

Download Full-text