Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms

Akatu, Wisdom; Khosravi, Marzieh; Mehedi, Md Abdullah Al; Mantey, Jones; Tohidi, Hamed; Shabanian, Hanieh

doi:10.20944/preprints202211.0437.v2

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Long short-term memory is a type of recurrent neural network frequently used for time series forecasting and is often used when variables are dependent on the previous data in the series [51,52]. Long short-term memory has the ability to capture the long-term dependencies among the predictor and target variables [53,54]. Long short-term memory feedback connections are the principal component of processing and recalling long-term information; this is a unique feature which differentiates it from a traditional multilayer perceptron method.…”

Section: Long Short-term Memorymentioning

confidence: 99%

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

Khosravi¹,

Baghalian²,

Burns³

et al. 2022

Preprint

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The alteration of natural land cover to impervious surfaces during development increases stormwater runoff. Stormwater Control Measures (SCMs) are used to manage water quantity and enhance water quality by restoring the hydrologic cycle altered by development. Often, SCMs have an outflow pipe to handle overflows or to manage the release of water detained when infiltration is not possible. Traditionally, these are static controls (e.g. a small orifice is used to restrict the volume of outflow), however, these systems can be improved by instituting real-time controls (RTC). RTC improve the functionality of SCMs by dynamically controlling outflows to adjust to environmental conditions. A major impediment to the widespread implementation of RTC is the high cost of installation and operation. This study utilized machine learning methods to develop a forecasting approach for the implementation of low-cost RTC that were implemented on a programmable gate of the outlet structure of a multi-stage basin in southeastern Pennsylvania. The goals were to decrease the peak flow exiting the basin during rain events, increase the volume of water detained, decrease the number of overtopping events, maintain healthy vegetation in the basin, and protect the downstream vegetation from erosion. Multiple popular data science algorithms were evaluated including multiple linear regression and long short-term memory. These algorithms were used with a dataset, which consisted of four years of historical sensor data, collected in 5-minute intervals, to train models to predict water levels to optimize operations. The accuracy of 30 models with three different methods of handling missing values were compared. A long short-term memory model configured with a 30-minute lead time produced the best results. Having an approximate same lag time of 30 minutes for the contributing drainage area of the SCM provided a sufficient RTC functioning period to improve the performance of the outlet structure.

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Section: Long Short-term Memorymentioning

confidence: 99%

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

Khosravi¹,

Baghalian²,

Burns³

et al. 2022

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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Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms

Cited by 4 publications

References 10 publications

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

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

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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