Thermal Investigation of Stromwater Management Ponds

Sabouri, F.; Gharabaghi, Bahram; McBean, Edward A.; Tu, C.

doi:10.14796/jwmm.c397

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Machine learning is frequently used to model the complex non-linear relationships in natural systems, including soil moisture, soil temperature, and stream habitat assessments [36][37][38][39]. Both ANN and GEP models have been developed to model the impact of anthropogenic heat sources, such as stormwater management ponds and impervious surfaces, on the receiving stream temperature [40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

et al. 2020

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Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

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Section: Introductionmentioning

confidence: 99%

Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

et al. 2020

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Urban stormwater thermal gene expression models for protection of sensitive receiving streams

Sattar

Gharabaghi

Sabouri

et al. 2017

Hydrological Processes

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Thermal impact of typical high-density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold, and coolwater streams. This is the first study of its kind that provides simple easy-to-use equations, developed using gene expression programming (GEP) that can guide the assessment and the design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed 3 GEP models using data collected during 3 years (2009-2011) from 4 urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90-0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis, we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.

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A Comparative Study Based on Long Short-Term Memory Networks for Assessing Photovoltaic Power

Majumdar

Bhattacharjee

Pal

et al. 2021

Computational Intelligence in Pattern Recognition

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Among the various types of renewable energy sources (RES), the photovoltaic (PV) generators play a very significant role in the production of electrical power, as the technology of these generators has been well perceived for a long period. These generators have been trusted to be environment-friendly. In this paper, mainly photovoltaic (PV) system-generated power has been designed and installed for energy production. This paper aims to present a solar power predictive analysis using deep neural networks (DNNs). A variant of recurrent neural network (RNN), namely long short-term memory (LSTM), has been used to model a photovoltaic (PV) grid-connected array output and approximate the generated power. A new algorithm is proposed for assessing the PV power for different array setups. An extensive comparative study using the DNN techniques is performed for evaluating different types of grid-connected array setups. This study is beneficial for the reader and useful for engineers and researchers interested in DNNs applied to solar photovoltaic systems data generation.

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Thermal Investigation of Stromwater Management Ponds

Cited by 6 publications

References 9 publications

Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

Urban stormwater thermal gene expression models for protection of sensitive receiving streams

A Comparative Study Based on Long Short-Term Memory Networks for Assessing Photovoltaic Power

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