Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients

Water Resources Management, Год журнала: 2024, Номер 38(10), С. 3953 - 3972

Опубликована: Апрель 19, 2024

Язык: Английский

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Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion

Environmental Modelling & Software, Год журнала: 2024, Номер 178, С. 106091 - 106091

Опубликована: Май 28, 2024

Язык: Английский

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Green solutions for blue waters: Using biomaterials to purify water from microplastics and nanoplastics

Journal of Water Process Engineering, Год журнала: 2024, Номер 65, С. 105854 - 105854

Опубликована: Июль 23, 2024

Язык: Английский

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Bending responses of graphene nanoplatelets reinforced sandwich cylindrical micro panel with piezoelectric layers

Mechanics of Advanced Materials and Structures, Год журнала: 2024, Номер unknown, С. 1 - 16

Опубликована: Авг. 4, 2024

Bending responses of a nanocomposite-reinforced cylindrical panel are studied in this article. A shell is assumed micro scale and sandwiched by piezoelectric layers. In article, micro-size dependent theory named as the modified couple stress (MCST) analytically employed kinematic relations extended through employing shear deformable model order to investigate electroelastic bending three-layered micro-shell bonded between smart layers subjected an applied voltage, external internal pressures. The rested on two parametrically elastic foundation. develop constitutive relations, mixture's rule well Halpin-Tsai utilized compute governing equations. Electroelastostatic obtained trigonometric functions. large parametric analysis presented explore deflection with change thickness layer radius, length radius ratio, different characteristics nanoplatelet reinforcement for both pressure. proposed composite electromechanical structure may be used structures systems. controllable system can suggested usage graphene nanoplatelets because flexibility affecting parameters.

Язык: Английский

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Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China

Agricultural Water Management, Год журнала: 2024, Номер 292, С. 108665 - 108665

Опубликована: Янв. 9, 2024

Accurate reference crop evapotranspiration (ET0) estimation is essential for agricultural water management, productivity, and irrigation systems. As the standard ET0 method, Penman-Monteith equation has been widely recommended worldwide. However, its application still restricted to comprehensive meteorological data deficiency, making exploration of alternative simpler models acceptable highly meaningful. Concerning aforementioned requirement, this study developed novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, regression component, estimate based on radiation-based (Rn-based), humidity-based (RH-based), temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), empirical models, result indicated that MA-CNN-BiLSTM achieved superior precision, with values Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), (RMSE), Absolute (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 d−1 strategy 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 0.294–0.699 strategy. Specifically, Rn-based excelled in temperate continental zone (TCZ) mountain plateau (MPZ), while RH-based yielded best precision others. Furthermore, was by 2.74–106.04% R2, 1.11–120.49% NSE, 1.41–40.27% RRMSE, 1.68–45.53% RMSE, 1.21–38.87% MAE, respectively. In summary, main contribution present proposal LSTM-type (MA-CNN-BiLSTM) cope various data-missing scenarios China, can provide effective support decision-making regional agriculture management.

Язык: Английский

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Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations

Journal of Hydrology, Год журнала: 2024, Номер 634, С. 131117 - 131117

Опубликована: Март 24, 2024

Global climate change has led to an increase in the frequency and scale of extreme weather events worldwide, there is urgent need develop better-performing hydrological models improve accuracy streamflow simulations facilitate water resource planning management. The Soil Water Assessment Tool (SWAT) a notable physical foundation widely used research. However, it uses simplified vegetation growth model, introducing uncertainty into simulation results. This study focused on improving model based remotely sensed phenological leaf area index (LAI) data. Phenological data were define dormancy, LAI replaced corresponding simulated by original model. approach improved describing dynamics. Then, enhanced SWAT was coupled with bidirectional long short-term memory (BiLSTM) validate processes upstream Hei River. During validation, performance simulating (R2 = 0.835, NSE 0.819) better than that 0.821, 0.805). In terms evapotranspiration, demonstrated even greater advantages. verification period, compared those R2 values for daily-scale increased from 0.196 −0.269 0.777 0.732, respectively. monthly-scale 0.782 0.678 0.906 0.851, Simultaneously, levels two coupling approaches prediction compared, i.e., direct BiLSTM (SWAT-BiLSTM) (enhanced SWAT-BiLSTM). results showed SWAT-BiLSTM always performed during entire especially which could more accurately predict peak changes. deep learning accuracy.

Язык: Английский

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Simultaneous Energy, Fresh Water, and Biogas Production Process Utilizing Solar Thermal and Sewage Sludge

Energy Science & Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

ABSTRACT Minimizing the detrimental effects of global warming and pollution from fossil fuel consumption is essential to meet growing demand for energy fresh water, making it imperative adopt renewable alternatives. The integration solar biomass in hybrid systems will grow importance. proposed study introduces a new design that facilitates simultaneous production power, biogas, water continuous process. present research aims tackle challenge utilizing multiple sources, such as biomass, generate fuel, water. To achieve this, 4‐stage multi‐effect desalination system be employed desalinating seawater. This paper discusses combining feedstocks address maintaining consistent power plants at night, when there no sunlight. hand involves assessing various factors using ASPEN Plus software, heat transfer fluid (SHTF), sewage sludge flowrates, biogas production, output waste stream gasification reactor, generation, freshwater production. Additionally, payback period this project approximately 4.8 years, with net value (NPV) around 560 million dollars. By performing sensitivity analysis, viability designed process quality resulting products were effectively demonstrated. From process, an impressive 76.8586 tons per hour syngas, composed carbon monoxide hydrogen, was generated. reached 34.547 MW, while simultaneously producing 783 m 3 /h Due efficient recovery throughout entire only 25 MW required. Despite efforts, operating 50% productivity level. supply required during daylight hours, total 38,908 square meters Parabolic trough collector (PTC) necessary. According environmental primary concern effect on human health. Solar collectors sea units account over 95% pollution. revelation showed resources could provide sustainable solution rising electricity, fuel.

Язык: Английский

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Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting

Journal of Hydrology, Год журнала: 2024, Номер 636, С. 131275 - 131275

Опубликована: Май 7, 2024

Язык: Английский

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Bending-based solution methodology using eigenvalue-eigenvector approach for analysis of foldable reinforced Golf Clubs cylindrical shell

Mechanics of Advanced Materials and Structures, Год журнала: 2024, Номер unknown, С. 1 - 14

Опубликована: Июль 21, 2024

Язык: Английский

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Evaluation of the probiotic, anti-microbial, anti-biofilm, and safety properties of Levilactobacillus brevis Lb13H

LWT, Год журнала: 2024, Номер 207, С. 116636 - 116636

Опубликована: Авг. 15, 2024

In this research, the probiotic potential, anti-microbial, anti-biofilm, and safety properties of Levilactobacillus brevis Lb13H were investigated. Also, Gaussian Process Regression (GPR) model was applied to predict some experimental parameters. The strain demonstrated resistance acidic pH, gastric intestinal juices, bile salts. Additionally, exhibited 45.34% surface hydrophobicity, 36.55% auto-aggregation capacity, 26.30% co-aggregation, 10.20% adhesion Caco-2 cells, 42.57% cholesterol removal, 48.63% radical-scavenging properties. able inhibit Listeria monocytogenes cells by 31.30%, its competitive inhibition percentage 37.5%. Furthermore, replacement L. with measured at 22.10%. most significant antimicrobial effect observed against monocytogenes, while least noted Rhizopus stolonifer. Haemolytic activity, DNase production, biogenic amine production not in strain, which also found be sensitive antibiotics ciprofloxacin chloramphenicol. cell-free supernatant effectively inhibited degraded biofilm formation achieving effectiveness rates 40.80% 36.60% respectively. results modeling indicated that there is any difference between actual predicted data GPR can variables high accuracy.

Язык: Английский

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