Related references
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Article
Engineering, Civil
Chuqiang Chen et al.
Summary: Accurately predicting water quality is essential in water resource management, and data-driven models such as neural networks are effective in this task. High-quality original datasets are necessary to build accurate prediction models, and this paper proposes a novel piecewise multivariate imputation (PWIMP) method to deal with missing data in water quality series. Wavelet shrinkage denoising based on the maximal overlap discrete wavelet transform (MODWT) is also used to reduce noise and improve prediction accuracy. Evaluation of four datasets generated by different imputation methods shows that PWIMP outperforms GLIMP and is competitive with coupling GLIMP and MODWT. Coupling PWIMP and MODWT significantly outperforms the other three models.
JOURNAL OF HYDROLOGY
(2023)
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Engineering, Civil
Feng Huang et al.
Summary: This study proposed a framework to address water temperature data scarcity by using the LSTM network and time series technologies. It was applied in the Dongting Lake Basin, China, and found that the water temperature showed different trends and changing rates across sub-basins and months.
JOURNAL OF HYDROLOGY
(2023)
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Engineering, Civil
F. Fuso et al.
Summary: The habitat quality of alpine rivers is greatly affected by human activity, particularly the exploitation for hydropower. This can lead to changes in river ecosystems and less suitable conditions for fish due to alterations in water temperature. A new methodology is presented to assess water temperature in rivers used for hydropower, which can be used to project potential deterioration of river habitats in response to increasing water temperature. In the case study of the Serio River in Northern Italy, which is heavily exploited by hydropower and inhabited by brown trout, a set of tools is proposed to evaluate the natural hydrological regime, assess water withdrawal, evaluate hydraulic suitability for trout based on hydraulic features, and assess changes in water temperature using a new model.
JOURNAL OF HYDROLOGY
(2023)
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Environmental Sciences
S. Rehana et al.
Summary: This study developed hybrid models using machine learning techniques to predict river water temperatures and assess the impact of climate change. The models effectively predicted monthly river water temperatures and outperformed other models.
WATER RESOURCES RESEARCH
(2023)
Article
Engineering, Marine
Rana Muhammad Adnan Ikram et al.
Summary: In this study, deep learning models (CNN and LSTM) combined with two optimization algorithms (RSA and INFO) were used to accurately estimate the daily water temperature of the Bailong River in China. The experimental results showed that the LSTM-INFO model with DOY input had higher accuracy in predicting daily water temperature.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Environmental
Saeid Mehdizadeh et al.
Summary: In this study, gene expression programming (GEP) and support vector machine (SVM) models were used to model the soil temperature at different depths in two weather stations in Iran. The results showed that the entropy-based GEP and SVM models had higher accuracy compared to the tau-Kendall-based standalone models. Furthermore, the hybrid models, which combined the wavelet theory with GEP and SVM, outperformed the single models in modeling the soil temperature.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
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Computer Science, Artificial Intelligence
Yunyu Wei et al.
Summary: A multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed for ship motion multi-step prediction. The proposed model effectively improves the prediction performance and outperforms other traditional and state-of-the-art models in the field of ship motion prediction. It has the potential for practical application in ship marine operations and can be used as an effective aid to ship warning systems.
ADVANCED ENGINEERING INFORMATICS
(2023)
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Wahiba Bounoua et al.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
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Biodiversity Conservation
Zijun Xiao et al.
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ECOLOGICAL INDICATORS
(2023)
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Computer Science, Interdisciplinary Applications
Zina Souaissi et al.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
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Environmental Sciences
Yuting Li et al.
Summary: This study proposed a new hybrid model based on LSTM neural network, combining the dual-stage attention mechanism and boundary corrected maximal overlap discrete wavelet transform data decomposition method. Compared with other models, the hybrid model showed higher prediction accuracy and provided early warning for NH3-N pollution.
ENVIRONMENTAL RESEARCH
(2023)
Article
Agriculture, Dairy & Animal Science
Wikit Phinrub et al.
Summary: This study investigated the effects of cold and heat shocks on serum electrolyte and biochemical changes in Panagasianodon gigas. Different temperature ranges were used for cold and heat shocks. The Na+ level was lowest in the 25-13 degrees C and 25-37 degrees C groups, and highest in the 25-31 degrees C group. K+ significantly increased with decreasing water temperature. Cl- significantly decreased with decreasing water temperature and increased with increasing water temperature. Serum CO2 level was affected by cold shock but not by heat shock. The appropriate temperature range for Panagasianodon gigas was found to be 25-28 degrees C.
JOURNAL OF APPLIED ANIMAL RESEARCH
(2023)
Article
Biology
Mayumi Yoshimura
Summary: Water temperature is a crucial factor for freshwater invertebrates, and it varies with air temperature. The study focuses on the effect of water temperature on the egg development of Stavsolus japonicus and the response of stoneflies with long egg periods to climate change. Water temperatures before 43 days prior to hatching do not affect the egg development in Stavsolus japonicus. Instead, they use egg diapause as an adaptive strategy to survive hot summer conditions. Increased water temperatures may lead to migration and stranded populations for stoneflies with lower adaptability in their egg development period, ultimately resulting in species extinction and reduced biodiversity in ecosystems.
JOURNAL OF THERMAL BIOLOGY
(2023)
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Engineering, Marine
Chenhua Ni et al.
Summary: This paper provides a brief review of the two typical approaches for predicting wave energy generation: physical models and data-driven models. It proposes a hybrid deep learning model that combines empirical wavelet transform (EWT) and a convolutional neural network (CNN) for wave power prediction. The EWT decomposes wave power observations into sub-bands with high and low frequencies, while the CNN model extracts spatial features from multi-dimensional grid data. The proposed hybrid model outperforms benchmark models based on time-space domain information, indicating its strong potential for wave energy exploitation.
Article
Environmental Sciences
N. Girgibo et al.
Summary: This study investigates how climate change has affected the Kvarken Archipelago by analyzing air temperature and water quality. The results show a significant correlation between air temperature and water temperature, leading to an increase in chlorophyll-a concentration, indicating an increase in phytoplankton growth and abundance in the water systems.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Environmental Sciences
Mathieu Auffray et al.
Summary: Small dams have a significant impact on stream temperatures, which are vital for aquatic ecosystems. This study aims to understand the effect of small dams on summer stream temperatures in a protected area in Canada. By comparing water temperatures upstream and downstream of small surface-release dams, the study assesses their effect on different attributes of the thermal regimes of streams. The results show that small dams contribute to an average warming of 3.7 degrees C downstream, similar to the warming effect of natural lakes. The study emphasizes the importance of understanding the cumulative effects of small reservoirs on stream temperature for the management of aquatic ecosystems.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Geography
Salim Heddam et al.
Summary: This paper demonstrates the superiority of the Bat-ELM model in river water temperature modeling in the Orda River, Poland. The results show that the Bat-ELM accurately predicts the temperature and outperforms other models in this aspect.
GEOGRAPHICAL JOURNAL
(2023)
Article
Environmental Sciences
Zihan Hao et al.
Summary: In this study, a new deep learning model called Attention-GRU was constructed to predict lake surface water temperature. The model extracted variable correlation information and temporal correlation information to achieve the most accurate prediction results for Qinghai Lake. The analysis revealed that air temperature was the dominant factor influencing the lake surface water temperature.
Article
Environmental Sciences
Kerry Staples et al.
Summary: Larval mosquito development is affected by water temperature, with shallow water being a common habitat. Existing mathematical models estimate water temperature using meteorological variables, and their complexity varies. We developed a modified heat balance model to estimate hourly water temperature and compared it to a model using only air temperature and water volume. Field measurements from a known larval habitat in Australia were used to assess the models. Publicly available air temperature and river height measurements were also analyzed for cost-effective long-term monitoring. The modified model had an average error of -0.5°C per hour, with air temperature as the second-best method (-2.82°C error). The public data sources accurately represented onsite water temperature measurements. The original heat balance model performed poorly in hot, dry, windy conditions. The modified model can be useful for larval mosquito development models and enhancing mosquito control activities.
Article
Environmental Sciences
Joanna Gizinska et al.
Summary: Climate change has a significant impact on the abiotic and biotic environment. This study aims to analyze the direction and extent of water temperature changes in eight rivers and three lakes on a monthly and annual basis. The results show that the average annual temperature rise in the Warta River basin reached 0.51 degrees C decade(-1), leading to an increase in mean annual water temperatures in lakes and rivers. The greatest changes in air temperature occurred in April, June, August, September, and November.
Article
Water Resources
M. A. Lorenzo-Gonzalez et al.
Summary: This study analyzes the changes in water temperature in the Ebro River and its main tributaries in northeast Spain, as well as the factors influencing them. The results show abnormal temperature increases downstream of nuclear power plants, lower temperature range near major reservoirs, and lower summer temperatures in large irrigation systems compared to stations with similar climates. Overall, there is a significant annual increase in water temperature, particularly downstream of the Ascó nuclear power plant. The increase is most pronounced in the spring months. These changes can be attributed to global warming and increased water consumption.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Biodiversity Conservation
Yongao Lu et al.
Summary: Temperature rhythm changes in outflows after reservoir construction cause thermal pollution in downstream rivers. Stratified water intake facilities can mitigate the impact of thermal pollution, but there is a lack of scientific guidance for their optimization and to meet downstream water temperature requirements. A new machine learning model based on the influence mechanism of outflow temperature and maximal information coefficient is constructed to predict the outflow temperature of thermally stratified reservoirs. The support vector regression (SVR) model with radial basis function (RBF) as the kernel function shows the best performance, with a mean absolute error of 0.112°C, root mean square error of 0.143°C, and Nash-Sutcliffe efficiency coefficient of 0.989. The proposed method provides an effective reference and scientific guidance for adaptive reservoir management.
ECOLOGICAL INDICATORS
(2023)
Article
Engineering, Civil
Qingshan Yang et al.
Summary: In this paper, a novel wind speed prediction method named EWT-ARIMA-LSSVM-GPR-DE-GWO method is proposed. This method decomposes the original wind speed signal into multiple IMF using EWT and uses various prediction models including ARIMA, LSSVM, and GPR to predict the IMF in sequence. DE-GWO algorithm is used to optimize the hyperparameters of LSSVM and GPR. The method shows superior performance in both accuracy and stability for wind speed prediction.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
(2023)
Article
Environmental Sciences
Fabio Di Nunno et al.
Summary: In this study, a novel machine learning algorithm was used to forecast daily lake surface water temperature using daily air temperature as the exogenous input variable. The prediction model showed very good forecasting capabilities for all lakes and forecast horizons, outperforming other models. The proposed model has significance for the scientific community in predicting lake surface water temperature.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Water Resources
Senlin Zhu et al.
Summary: In this study, a simple method using the air2water model was proposed to estimate the lake surface water temperature (LSWT) of lowland lakes in Poland. By collecting and analyzing parameter values of different lakes, model parameters suitable for Polish lowland lakes were derived and tested with satisfactory results. This method can be applied to unmonitored lakes using readily available daily air temperature data from meteorological stations.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Geosciences, Multidisciplinary
Manuel C. Almeida et al.
Summary: The prediction of river water temperature is crucial in environmental science, especially for low-order rivers with limited temperature datasets. In this study, five models were used to predict the water temperature of 83 rivers with a large scarcity of forcing datasets. The results emphasize the importance of hyperparameter optimization and suggest that all the models considered in this study are crucial when the number of predictor variables and observed river water temperature values are limited.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
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Farshad Ahmadi et al.
Summary: Estimation of monthly reservoir inflow in Iran's Maroon Dam reservoir utilizing climatic data showed that rainfall is the most important parameter affecting reservoir inflow. Proposed hybrid models combining random forest with complete ensemble empirical mode decomposition and wavelet analysis outperformed classic models in accuracy assessment.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
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Environmental Sciences
Salim Heddam et al.
Summary: This study evaluates the contribution of variational mode decomposition (VMD) to the improvement of machine learning models for predicting river water temperature. The results show that applying VMD as a preprocessing technique significantly enhances the accuracy of the models.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
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Engineering, Civil
Umesh K. Singh et al.
Summary: The study utilized a new multi-level ensemble machine learning approach, combining a voting model with three standalone ensemble techniques, to predict the critical shear stress of gravel particles in mixtures. The results indicated that the multi-level ensemble machine learning model outperformed the standalone techniques in accurately estimating CSS values.
JOURNAL OF HYDROLOGY
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Environmental Sciences
Dao Nguyen Khoi et al.
Summary: This study evaluated the performance of twelve machine learning models in estimating the surface water quality of the La Buong River in Vietnam, with extreme gradient boosting (XGBoost) showing the highest accuracy.
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Engineering, Environmental
Ali Aldrees et al.
Summary: Assessing river water quality is crucial for improving water resources management plans. Traditional methods for calculating water quality index are time-consuming and prone to errors, thus the adoption of reliable machine learning algorithms has become necessary. This study predicts water quality index, total dissolved solids, and electrical conductivity using machine learning techniques. The results show that using ensemble learners improves the performance of individual models.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
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Green & Sustainable Science & Technology
Sudha Bishnoi et al.
Summary: This study classified cotton genotypes based on continuous and categorical variables using machine learning classifiers/models. The results showed that random forest (RF) and AdaBoost algorithms performed the best in terms of accuracy, making them the optimal choice for classifying low- and high-yielding cotton genotypes.
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Computer Science, Interdisciplinary Applications
John Quilty et al.
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ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
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Materials Science, Characterization & Testing
Nawaf H. M. M. Shrifan et al.
Summary: By using a hybrid signal processing method and refinement feature extraction technique, this study has enhanced the imaging efficiency of disbond detection in composite materials. Through scanning with a specific waveguide and analyzing reflection signals, combined with Bi-LSTM network classification, successful disbond detection at the 1 mm scale with an accuracy rate of 88.84% has been achieved.
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Summary: There is a growing trend in employing ensemble learning methodologies in various engineering fields, including hydrology, for simulation and prediction purposes. The diversity of ensemble techniques available in hydrological sciences has led to the development and utilization of different strategies. The general findings suggest the superiority of using ensemble strategies over traditional model learning in hydrology, with boosting techniques being more commonly and successfully implemented compared to other methods.
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