Quantification and Prediction of the Impact of ENSO on Rainfed Rice Yields in Thailand

Environmental Challenges, Год журнала: 2025, Номер unknown, С. 101123 - 101123

Опубликована: Март 1, 2025

Язык: Английский

---

Quantifying and simulating the weather forecast uncertainty for advanced building control

Journal of Building Performance Simulation, Год журнала: 2025, Номер unknown, С. 1 - 16

Опубликована: Янв. 28, 2025

Язык: Английский

---

Improving Temperature Forecast Accuracy with Enhanced Stacking Operational Consensus Forecasts Algorithm

Опубликована: Янв. 1, 2025

Язык: Английский

---

Applying artificial intelligence to deal with the cold wave

Taḥqīq va tusi̒ah-i salāmata, Год журнала: 2025, Номер 2(4), С. 37 - 41

Опубликована: Янв. 1, 2025

Язык: Английский

---

Predicting Salinity Levels in the Mekong Delta (Viet Nam): Analysis of Machine Learning and Deep Learning Models

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 29, 2025

Salinity intrusion stands out as a severe yet escalating challenge facing the water resource management and agricultural production of Mekong Delta in Vietnam result climate change upstream hydrological changes. This paper assesses efficacy six different machine learning (ML) deep models (DL) for hourly prediction salinity at four stations (Cau Quan, Tra Vinh, Ben Trai, Tran De). The are XGB, GB, SVR, LSTM, RNN ANN. Using data 2015–2020 with discharge tidal levels major inputs we designed training testing (training: Jan 2015-mid 2018; testing: mid 2018-Feb 2020). Our results prove that LSTM XGB have best prediction. In particular, they showed good accuracy predicting (RMSE: 0.25 to 0.30, R² > 0.97) downstream 1.5 1.6, 0.88). success is due capacity high temporal resolution well spatio-temporal dynamics variation. structure has proven be effective capturing long-term dependencies, such seasonal patterns, while successfully non-linear interactions between greatest success, particularly discharge-tidal level interactions. ML/DL capable forecasting which can open doors data-driven Delta. Future studies should further add hydro-meteorological parameters, other hybrid architectures, real-time systems, could useful operationally wider applicability.

Язык: Английский

---

An Empirical Analysis of Deep Learning Models for Temperature Prediction

International Journal of experimental research and review, Год журнала: 2025, Номер 47, С. 12 - 24

Опубликована: Апрель 30, 2025

Accurate temperature prediction is critical in diverse areas, such as agriculture, disaster management, and urban planning, where understanding climatic patterns essential. This study explores the application of advanced deep-learning models for forecasting, focusing on model’s ability to establish complex relationships temporal dependencies within data. evaluates performance various using environmental Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), GRU (BiGRU) were developed compared. The trained meteorological data from Ranchi, India, spanning 2014-2024. Performance was assessed Root Mean Square Error (RMSE), loss function analysis, statistical significance testing. Results indicate that bidirectional architectures (BiLSTM BiGRU) consistently outperformed unidirectional models. BiLSTM achieved lowest RMSE most balanced values across training, validation test sets. model performed well by 39.19.7% 15.36% loss. From best performer compared with BiGRU, a negative t-statistic (-29.65) very low p-value (0.00000771), indicating statistically significant difference.

Язык: Английский

---

Investigation of Precipitation and Temperature Trends Using Classical and Innovative Approaches with Corresponding Frequencies in Antalya Basin, Türkiye.

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2025, Номер unknown, С. 103958 - 103958

Опубликована: Май 1, 2025

Язык: Английский

---

Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis

Agricultural Water Management, Год журнала: 2025, Номер 315, С. 109542 - 109542

Опубликована: Май 10, 2025

Язык: Английский

---

Multidimensional analysis of climate-induced streamflow variability using CMIP6 data and advanced modeling techniques

Geocarto International, Год журнала: 2025, Номер 40(1)

Опубликована: Май 19, 2025

Язык: Английский

---

Predicting salinity levels in the Mekong delta (Viet Nam): analysis of machine learning and deep learning models

Discover Artificial Intelligence, Год журнала: 2025, Номер 5(1)

Опубликована: Май 29, 2025

Язык: Английский

---