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

Khosravi, Marzieh; Ghoochani, Shima; Nazemi, Neda

doi:10.20944/preprints202305.1167.v1

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…Water quality and quantity data have not been widely investigated in previous work employing LSTM. The proposed LSTM model only needs a straightforward data preprocessing method, as opposed to the MLP model mentioned earlier [55]. The LSTM neural network exhibits a recurrent nature, with interconnected units forming a directed cycle that enables data to flow in both forward and backward directions within the network.…”

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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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

Self Cite

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“…Drinking water quality is a paramount concern worldwide, as it directly affects human health and well-being. Access to clean and safe drinking water is vital for preventing waterborne diseases and ensuring public health [5][6][7][8]. The assessment and management of drinking water quality have emerged as crucial fields, addressing the need for continuous monitoring and maintenance of water supplies.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection

Ghoochani,

Khorram,

Nazemi

2023

Preprint

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Water quality assessments are crucial for human health and environmental safeguards. The utilization of a subset of artificial intelligence such as Machine Learning (ML) presents significant impacts to enhance the prediction and classification of water quality. In this research, a set of diverse ML algorithms was evaluated to handle a comprehensive dataset of water quality measurements over an extended period. The aim was to develop a robust approach for accurately forecasting water quality. This approach employed machine learning classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Gaussian Process Classification (GPC), Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). The water quality parameters assessed for pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes and turbidity. The XGBoost model exhibited the highest accuracy of 89.47% among the classifiers and Stacked Ensemble Classifiers (SEC) improved the prediction further to 92.98%. The findings suggest that XGBoost and the SEC hold promise as reliable approaches for water quality assessments in contrast of artificial intelligence.

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“…Given the potential risks associated with thunderstorms, it is essential to monitor weather trends and take the necessary precautions to lessen their impacts, this entails putting policies in place like evacuation plans and construction regulations made to resist extreme weather [20,21]. Forecasting of climatic and hydrological variables normally require time series analysis, use of physically intensive models that requires calibration and validation with observed dataset of different physically important parameters; or data intensive model that require machine learning and artificial neural network analysis [22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Response of Severe Thunderstorm Environments in Belgium to Climate Change

Duti,

Gysegem,

Khosravi

et al. 2023

Preprint

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Strong convective storms can be a serious threat to modern society, influencing both the economy and human life. Thunderstorms can bring much-needed rain after a dry spell, but the nature of thunderstorm mostly causes damage due to high wind associated with it and heavy intensity rain that mostly causes quick overlandflow and flashfloods. The paper focused on the 2011 Pukkelpop event in Belgium where due to heavy thunderstorm a lot of financial damage occurred, and five casualties. In this paper we look into the basic elements that enforces the creation of a thunderstorm like CAPE and windshear. A representative grid has been taken from the Alaro-0 model of 12.5 km resolution for the analysis of the thunderstorm over Belgium which is detailed out in the methodology. The model runs of Alaro-0 from Royal Meteorological Institute of Belgium (RMI) is used for historical and future years for CAPE and Shear. We look into STEnv which is a composite number that can provide some insight on occurrence of thunderstorm, and STEnv is when CAPE*Shear exceeds certain threshold. In this study the threshold has been chosen as 97%. Only summer months have been taken into account into this study as summer(s) has the most thunderstorm in Belgium. The analysis of CAPE and STEnv over historical period (1977-2005), near future (2041-2069) and far future (2071-2099) shows significant increase in occurrence of thunderstorm in the 2071-2099s, specially in extreme cases such as 97% and 99% quantile. CAPE (J/Kg) increases in all future scenarios compared to the historical period. In 2071-2099s, CAPE increases from 2100 J/Kg to 2300 J/Kg (90% quantile) and from 3500 J/Kg to 3900 J/Kg (99% quantile) compared to the historical period. The number of STEnv increases from 300 to 378 in top 10% storm case (90% quantile), 158 to 218 in top 5% storm (95% quantile), 97 to 153 in top 3% storm (97% quantile) and 33 to 66 in top 1% storm (99% quantile) with a 90% confidence interval overall. Shear mostly remains unchanged. The study also shows spatial variation of CAPE, shear and STEnv over Belgium for present and future scenarios in different extremities (90%, 95%, 97% and 99% quantiles). There is a good indication that increase in CAPE translates to increase in STEnv, which is an accepted measure to predict occurrence of thunderstorm. Future climate change also amplifies the occurrence of thunderstorm, which is coherent with relevant studies. The uncertainty related to the study can be reduced with more detailed dataset and involvement of numerical weather prediction models beyond data statistics.

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

Cited by 3 publications

References 44 publications

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

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

Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection

Response of Severe Thunderstorm Environments in Belgium to Climate Change

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