Using Machine Learning to Improve Performance of a Low-Cost Real-Time Stormwater Control Measure

Khosravi, Marzieh; Mehedi, Md Abdullah Al; Baghalian, Sara; Burns, Michael J.; Welker, Andrea L.; Golub, Michael A.

doi:10.20944/preprints202211.0519.v1

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…In this research, a neural network with one LSTM hidden unit accompanied by a dense layer connecting the LSTM target output at the last time-step (t-1) to a single output neuron with non-linear activation function. The LSTM model was trained using the deep learning library, Keras in Python, and the RMSE, and MAE [54][55][56][57][58]. To predict the discharge variable of a time-step in the future daily values of the variables at the previous time-steps are used.…”

Section: Long Short-term Memory (Lstm) Recurrent Neuralmentioning

confidence: 99%

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Khosravi¹,

Ghoochani²,

Nazemi³

2023

Preprint

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The intrinsic non-linearity and complexity of suspended sediment dynamics, which are impacted by the geographical variability of basin parameters and temporal climatic patterns, make it difficult to estimate suspended sediment concentration (SSC) accurately in hydrological processes. Deep neural networks (DNNs), a cutting-edge modeling method that can capture the innate non-linearity in hydrological systems, have emerged as a solution to this problem. Using primary data on discharge, SSC, and turbidity, the long short-term memory method of DNNs was employed in this study to simulate the discharge-suspended sediment connection for the Esopus Creek, NY, USA as the first objective. To develop effective modeling strategies, multiple scenarios of feature selection, including combinations of discharge, turbidity and SSC for preceding days, were considered. Secondly, the variations in SSC and discharge between the three USGS gages along Esopus Creek that were upper stream, lower stream, and downstream, adjacent to the pourpoint were analyzed. Statistical metrics and scenarios of feature importance were used to evaluate the effectiveness of the DNN-based models. The study shows that among 24 DNN approaches feature selection is crucial in simulating the daily SSC, and discharge and presenting novel research directions for utilizing machine learning algorithms in water quality.

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Section: Long Short-term Memory (Lstm) Recurrent Neuralmentioning

confidence: 99%

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Khosravi¹,

Ghoochani²,

Nazemi³

2023

Preprint

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“…LSTM model have already been used as a very advanced model in the field of DL, e.g., speech recognition, natural language processing, automatic image captioning and machine translation [44,54,55]. However, only a few studies have applied Recurrent Neural Networks (RNNs) or LSTMs to forecast multivariate time series data in the field of water resource [56][57][58]. The objective of this research is to untangle the pattern of the temporal distribution and linkage among the aforementioned SW variables and perform predictive analysis on the using the previous observed data.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network

Ghoochani¹,

Nazemi²

2023

Preprint

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Multivariate predictive analysis of the Stream-Water (SW) parameters (discharge, water level, temperature, dissolved oxygen, pH, turbidity, and specific conductance) is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the meteorological and climatic features have a significant impact on the temporal distribution of the SW variables in recent days making the SW variables forecasting even more complicated for diversified water-related issues. To predict the SW variables, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-informed analysis and prediction, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, a comprehensive Explorative Data Analysis (EDA) and feature engineering were performed to prepare the dataset to obtain the best performance of the predictive model. Long Short-Term Memory (LSTM) neural network regression model is trained using over several years of daily data to predict the SW variables up to one week ahead of time (lead time) with satisfactory performance. The performance of the proposed model is found highly adequate through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and a set of error matrices. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time SW variables prediction.

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“…The LSTM model has been employed extensively in various fields of deep learning, such as speech recognition, natural language processing, automatic image captioning, and machine translation, showcasing its advanced capabilities [47,56,57]. In the realm of water resource forecasting, there have been limited instances where researchers have utilized Recurrent Neural Networks (RNNs) or LSTMs to predict multivariate time-series data [58][59][60]. The objective of this research is to untangle the pattern of the temporal distribution and the relation among the selected SW (surface water) variables for this study; as well as perform predictive analysis using observed data.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multi-Step Long Short-Term Memory Neural Network for Simultaneous Stream-Water Variable Prediction

Khosravi

Duti

Yazdan

et al. 2023

Eng

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Implementing multivariate predictive analysis to ascertain stream-water (SW) parameters including dissolved oxygen, specific conductance, discharge, water level, temperature, pH, and turbidity is crucial in the field of water resource management. This is especially important during a time of rapid climate change, where weather patterns are constantly changing, making it difficult to forecast these SW variables accurately for different water-related problems. Various numerical models based on physics are utilized to forecast the variables associated with surface water (SW). These models rely on numerous hydrologic parameters and require extensive laboratory investigation and calibration to minimize uncertainty. However, with the emergence of data-driven analysis and prediction methods, deep-learning algorithms have demonstrated satisfactory performance in handling sequential data. In this study, a comprehensive Exploratory Data Analysis (EDA) and feature engineering were conducted to prepare the dataset, ensuring optimal performance of the predictive model. A neural network regression model known as Long Short-Term Memory (LSTM) was trained using several years of daily data, enabling the prediction of SW variables up to one week in advance (referred to as lead time) with satisfactory accuracy. The model’s performance was evaluated by comparing the predicted data with observed data, analyzing the error distribution, and utilizing error matrices. Improved performance was achieved by increasing the number of epochs and fine-tuning hyperparameters. By applying proper feature engineering and optimization, this model can be adapted to other locations to facilitate univariate predictive analysis and potentially support the real-time prediction of SW variables.

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Using Machine Learning to Improve Performance of a Low-Cost Real-Time Stormwater Control Measure

Cited by 8 publications

References 51 publications

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network

Multivariate Multi-Step Long Short-Term Memory Neural Network for Simultaneous Stream-Water Variable Prediction

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