Water Temperature Prediction Using Improved Deep Learning Methods through Reptile Search Algorithm and Weighted Mean of Vectors Optimizer

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(2), P. 259 - 259

Published: Jan. 23, 2023

Precise estimation of water temperature plays a key role in environmental impact assessment, aquatic ecosystems’ management and resources planning management. In the current study, convolutional neural networks (CNN) long short-term memory (LSTM) network-based deep learning models were examined to estimate daily temperatures Bailong River China. Two novel optimization algorithms, namely reptile search algorithm (RSA) weighted mean vectors optimizer (INFO), integrated with both enhance their prediction performance. To evaluate accuracy implemented models, four statistical indicators, i.e., root square errors (RMSE), absolute errors, determination coefficient Nash–Sutcliffe efficiency utilized on basis different input combinations involving air temperature, streamflow, precipitation, sediment flows day year (DOY) parameters. It was found that LSTM-INFO model DOY outperformed other competing by considerably reducing RMSE MAE predicting temperature.

Language: Английский

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Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 61, P. 102485 - 102485

Published: March 26, 2024

Language: Английский

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Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(3), P. 1141 - 1164

Published: Jan. 3, 2024

Language: Английский

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Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

Environmental Research Letters, Journal Year: 2020, Volume and Issue: unknown

Published: Dec. 18, 2020

Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts strongly affected by groundwater-surface interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due parameter equifinality. Based on the long short-term memory (LSTM) deep learning architecture, we developed basin-centric lumped daily mean model, trained over 118 data-rich basins no major dams in conterminous United States, and showed strong results. At national scale, obtained median root-mean-square error 0.69°C, Nash–Sutcliffe model efficiency coefficient 0.985, correlation 0.994, are marked improvements previous values reported literature. The addition observations as input elevated performance this model. In absence measured streamflow, that two-stage could used, where simulated pre-trained LSTM (Qsim) still benefited even though new brought directly into inputs indirectly used provided during training Qsim, potentially improve internal representation physically meaningful variables. Our results indicate relationships exist between basin-averaged forcing variables, catchment attributes, single data continental scale.

Language: Английский

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Machine-learning methods for stream water temperature prediction

Hydrology and earth system sciences, Journal Year: 2021, Volume and Issue: 25(5), P. 2951 - 2977

Published: May 31, 2021

Abstract. Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well socio-economic conditions within catchment. The development of modelling concepts for predicting river water and will be essential effective integrated management adaptation strategies future global changes (e.g. climate change). This study tests performance six different machine-learning models: step-wise linear regression, random forest, eXtreme Gradient Boosting (XGBoost), feed-forward neural networks (FNNs), two types recurrent (RNNs). All models are applied using data inputs daily prediction 10 Austrian catchments ranging from 200 96 000 km2 exhibiting wide range physiographic characteristics. evaluated input sets include combinations means air temperature, runoff, precipitation radiation. Bayesian optimization optimize hyperparameters all models. To make results comparable previous studies, widely used benchmark additionally: regression air2stream. With mean root squared error (RMSE) 0.55 ∘C, tested could significantly improve compared (1.55 ∘C) air2stream (0.98 ∘C). In general, show very similar models, median RMSE difference 0.08 ∘C between From both FNNs XGBoost performed best 4 catchments. RNNs best-performing largest catchment, indicating that mainly perform when processes long-term dependencies important. Furthermore, was observed hyperparameter showing importance optimization. Especially FNN model showed an extremely large standard deviation 1.60 due chosen hyperparameters. evaluates variables, training characteristics stream prediction, acting basis regional multi-catchment preprocessing steps implemented open-source R package wateRtemp provide easy access these approaches facilitate further research.

Language: Английский

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Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization

Granular Computing, Journal Year: 2021, Volume and Issue: 7(2), P. 411 - 420

Published: June 22, 2021

Language: Английский

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Impact of climate change on river water temperature and dissolved oxygen: Indian riverine thermal regimes

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: June 2, 2022

Abstract The impact of climate change on the oxygen saturation content world’s surface waters is a significant topic for future water quality in warming environment. While increasing river temperatures (RWTs) with signals have been subject several recent research, how affects Dissolved Oxygen (DO) levels not intensively studied. This study examined direct effect rising RWTs saturated DO concentrations. For this, hybrid deep learning model using Long Short-Term Memory integrated k-nearest neighbor bootstrap resampling algorithm developed RWT prediction addressing sparse spatiotemporal data seven major polluted catchments India at monthly scale. summer increase Tunga-Bhadra, Sabarmati, Musi, Ganga, and Narmada basins are predicted as 3.1, 3.8, 5.8, 7.3, 7.8 °C, respectively, 2071–2100 ensemble NASA Earth Exchange Global Daily Downscaled Projections air temperature Representative Concentration Pathway 8.5 scenario. increases up to7 °C summer, reaching close to 35 decreases capacity by 2–12% 2071–2100. Overall, every 1 increase, there will be about 2.3% decrease level concentrations over Indian under signals.

Language: Английский

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Low-Cost Internet-of-Things Water-Quality Monitoring System for Rural Areas

Sensors, Journal Year: 2023, Volume and Issue: 23(8), P. 3919 - 3919

Published: April 12, 2023

Water is a vital source for life and natural environments. This the reason why water sources should be constantly monitored in order to detect any pollutants that might jeopardize quality of water. paper presents low-cost internet-of-things system capable measuring reporting different sources. It comprises following components: Arduino UNO board, Bluetooth module BT04, temperature sensor DS18B20, pH sensor-SEN0161, TDS sensor-SEN0244, turbidity sensor-SKU SEN0189. The will controlled managed from mobile application, which monitor actual status We propose evaluate five rural settlement. results show most we have are proper consumption, with single exception where values not within limits, as they outperform maximum accepted value 500 ppm.

Language: Английский

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A state-of-the-art review of long short-term memory models with applications in hydrology and water resources

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112352 - 112352

Published: Oct. 1, 2024

Language: Английский

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Evaluating the effect of runoff on an agricultural retention stormwater pond temperature using an experimentally validated TRNSYS model

Sustainable Water Resources Management, Journal Year: 2025, Volume and Issue: 11(2)

Published: Feb. 4, 2025

Language: Английский

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