Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

Hydrology, Год журнала: 2023, Номер 10(3), С. 58 - 58

Опубликована: Фев. 27, 2023

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, standardized precipitation index (SPI) was monitored predicted Peru between 1990 2015. The study proposed a hybrid model, called ANN-FA, for SPI time scales (SPI3, SPI6, SPI18, SPI24). A state-of-the-art firefly algorithm (FA) has been documented as powerful tool to support modeling issues. ANN-FA uses an artificial neural network (ANN) which is coupled with FA Lima via other stations. Through intelligent utilization series from neighbors’ stations model inputs, suggested approach might be used forecast at meteorological station insufficient data. To conduct this, SPI3, SPI24 were modeled using stations’ datasets Peru. Various error criteria employed investigate performance model. Results showed that effective promising drought also multi-station strategy lack results can help predict mean absolute = 0.22, root square 0.29, Pearson correlation coefficient 0.94, agreement 0.97 testing phase best estimation (SPI3).

Язык: Английский

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Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study

Journal of Environmental Management, Год журнала: 2024, Номер 354, С. 120246 - 120246

Опубликована: Фев. 14, 2024

Язык: Английский

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A coupled extreme gradient boosting-MPA approach for estimating daily reference evapotranspiration

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(2)

Опубликована: Янв. 16, 2025

Язык: Английский

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A review of the Artificial Intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives

Computers and Electronics in Agriculture, Год журнала: 2023, Номер 209, С. 107836 - 107836

Опубликована: Апрель 28, 2023

Язык: Английский

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Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Journal of the Science of Food and Agriculture, Год журнала: 2024, Номер 104(10), С. 6208 - 6220

Опубликована: Март 7, 2024

Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution moisture during dehydration glutinous rice. The hyperparameters models optimized with three strategies: Bayesian optimization, grid search random search. To understand parameters that facilitate model adaptation process, global sensitivity analysis (GSA) was compute impact input variables on output.

Язык: Английский

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A novel hybrid modeling approach based on empirical methods, PSO, XGBoost, and multiple GCMs for forecasting long-term reference evapotranspiration in a data scarce-area

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 232, С. 110106 - 110106

Опубликована: Фев. 12, 2025

Язык: Английский

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Double-layer home energy management strategy for increasing PV self-consumption and cost reduction through appliances scheduling, EV, and storage

Energy Reports, Год журнала: 2023, Номер 10, С. 3494 - 3518

Опубликована: Окт. 13, 2023

Maximizing self-consumption of the photovoltaic (PV) generation is an important factor to increase penetration PV in residential grid. It can improve system profitability, save energy and reduce grid stress. This study proposes a double-layer home management strategy household electricity costs. The first layer involves rescheduling shiftable appliances operate during surplus hours, while second employs multi-objective based on Jaya particle swarm optimization (PSO) algorithms optimize power exchange between storage (ESS) electric vehicle (EV), with smart home. Six different scenarios are simulated investigate role appliances, ESS, EV technology increasing self-consumption. Results scheduling showed that proposed consumption by 17–41% 27–78%, respectively, depending scenarios. found be more effective than single-layer approach, significant economic benefits. method provides efficient solution for improving saving energy, reducing Thus, this contributes development sustainable systems, paving way future research area.

Язык: Английский

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A novel regenerative braking energy recuperation system for electric vehicles based on driving style

Energy, Год журнала: 2023, Номер 283, С. 129055 - 129055

Опубликована: Сен. 12, 2023

Язык: Английский

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The projected futures of water resources vulnerability under climate and socioeconomic change in the Yangtze River Basin, China

Ecological Indicators, Год журнала: 2023, Номер 147, С. 109933 - 109933

Опубликована: Янв. 25, 2023

Despite being one of the most abundant water resources globally, Yangtze River Basin (YRB) region is facing substantial risks aggravated by climatic and anthropogenic changes. Here, we adopted an integrated framework to investigate plausible futures resource provisioning in YRB under current future conditions on sub-watershed level: (i) a process-based model (InVEST) was used project yield whole nearest (2040–2060) distant (2080–2100) (ii) socio-ecological index developed assess spatio-temporal patterns vulnerability (WRV). Model projections indicated that several water-rich areas southeastern would suffer declining future. While projected increase some drier regions northwest. Future changes basin-level were decrease low emissions scenarios (RCP2.6) combined with sustainability socioeconomic scenario (SSP1). The greatest medium-to-high end (RCP7.0) rather than high-end (RCP8.5) climate change scenario. high WRV distributed sub-watersheds near Taihu Lake source River. Climate have different roles shaping dynamics WRV, precipitation reduction consumption likely result increased levels lower reaches middle reaches, respectively. Our study added new spatial data for vital ecological economic importance Asia. prone should be prioritised management practices. assessment approach this concurrent measures from both subjective objective perspectives could relevant studies exploring how respond environmental

Язык: Английский

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Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions

Sustainability, Год журнала: 2023, Номер 15(21), С. 15494 - 15494

Опубликована: Окт. 31, 2023

Reference evapotranspiration (ET0) is critical in agriculture and irrigation water management, particularly arid semi-arid regions. Our study aimed to develop an accurate efficient model for estimating ET0 using various climatic variables as predictors. This research evaluated two techniques, i.e., stepwise regression artificial neural networks (ANNs), identify the most effective calculating ET0. The models were developed tested based on climate data obtained from whole station of Egypt. CLIMWAT 2.0 program was used acquire Egypt a total 32 stations. software dedicated meteorological database created specifically work with CROPWAT computer program. average spanning 29 years, 1991 2020. utilized compute reference 8, Penman–Monteith equation. results showed that ANN demonstrated superior performance calculations compared other methods, achieving coefficient determination (R2) 0.99 mean absolute percentage error (MAPE) 2.7%. In contrast, yielded R2 0.95 MAPE 8.06. On hand, influential maximum temperature, humidity, solar radiation, wind speed. findings this could be applied fields, such agriculture, irrigation, crop requirements, optimize growth under limited resources global environmental changes. Furthermore, our identifies limitations challenges applying these regions, availability constraints complexity. We discuss need more extensive reliable datasets suggest future directions, including ensemble modeling, remote sensing integration, evaluating change’s impact estimation. Overall, contributes understanding estimation regions provides valuable insights into applicability ANNs. ANNs offers potential advancements resource management agricultural planning, enabling informed decision-making processes.

Язык: Английский

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