Enhancing prediction of dissolved oxygen over Santa Margarita River: Long short-term memory incorporated with multi-objective observer-teacher-learner optimization

Journal of Water Process Engineering, Год журнала: 2025, Номер 70, С. 106969 - 106969

Опубликована: Янв. 11, 2025

Язык: Английский

---

Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP)

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 27, 2025

Язык: Английский

---

An integrated hybrid deep learning data driven approaches for spatiotemporal mapping of land susceptibility to salt/dust emissions

Advances in Space Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

---

Dissolved Oxygen Forecasting for Lake Erie’s Central Basin Using Hybrid Long Short-Term Memory and Gated Recurrent Unit Networks

Water, Год журнала: 2024, Номер 16(5), С. 707 - 707

Опубликована: Фев. 28, 2024

Dissolved oxygen (DO) concentration is a pivotal determinant of water quality in freshwater lake ecosystems. However, rapid population growth and discharge polluted wastewater, urban stormwater runoff, agricultural non-point source pollution runoff have triggered significant decline DO levels Lake Erie other lakes located populated temperate regions the globe. Over eleven million people rely on Erie, which has been adversely impacted by anthropogenic stressors resulting deficient concentrations near bottom Erie’s Central Basin for extended periods. In past, hybrid long short-term memory (LSTM) models successfully used time-series forecasting rivers ponds. prediction errors tend to grow significantly with period. Therefore, this research aimed improve accuracy taking advantage real-time (water temperature concentration) monitoring network establish temporal spatial links between adjacent stations. We developed LSTM that combine LSTM, convolutional neuron (CNN-LSTM), CNN gated recurrent unit (CNN-GRU) models, (ConvLSTM) forecast near-bottom Basin. These their capacity handle complicated datasets variability. can serve as accurate reliable tools help environmental protection agencies better access manage health these vital Following analysis 21-site dataset 2020 2021, ConvLSTM model emerged most reliable, boasting an MSE 0.51 mg/L, MAE 0.42 R-squared 0.95 over 12 h range. The foresees future hypoxia Erie. Notably, site 713 holds significance indicated outcomes derived from Shapley additive explanations (SHAP).

Язык: Английский

---

Hybrid deep learning based prediction for water quality of plain watershed

Environmental Research, Год журнала: 2024, Номер 262, С. 119911 - 119911

Опубликована: Сен. 2, 2024

Язык: Английский

---

A long-term multivariate time series prediction model for dissolved oxygen

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102695 - 102695

Опубликована: Июнь 20, 2024

Accurate and efficient long-term prediction of marine dissolved oxygen (DO) is crucial for the sustainable development aquaculture. However, multidimensional time dependency lag effects chemical variables present significant challenges when handling multiple inputs in univariate tasks. To address these issues, we designed a multivariate time-series model (LMFormer) based on Transformer architecture. The proposed decomposition strategy effectively leverages feature information at different scales, thereby reducing loss critical information. Additionally, dynamic variable selection gating mechanism was to optimize collinearity problem data extraction process. Finally, an two-stage attention architecture capture long-range dependencies between features. This study conducted high-precision 7-day advance DO predictions two case studies, environmentally stable Shandong Peninsula China San Juan Islands United States, which are affected by extreme conditions such as ocean currents. experimental results demonstrate superior performance generalizability model. In case, mean absolute error (MAE), root square (RMSE), coefficient determination (R2), Kling–Gupta efficiency (KGE) reached 0.0159, 0.126, 0.9743, 0.9625, respectively. MAE reduced average 42.34% compared that baseline model, RMSE 24.57%, R2 increased 22.54%, KGE improved 12.04%. Overall, achieves data, providing valuable references management decision-making

Язык: Английский

---

Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6

Agriculture, Год журнала: 2024, Номер 14(9), С. 1556 - 1556

Опубликована: Сен. 9, 2024

One of the primary factors in hydrological cycle is reference evapotranspiration (ET0). The prediction ET0 crucial to manage irrigation water agriculture under climate change; however, little research has been conducted on trends changes Shandong Province. In this study, estimate entire Province, 245 sites were chosen, and monthly values during 1901–2020 computed using Hargreaves–Samani formula. A deep learning model, termed SAO-CNN-BiGRU-Attention, was utilized forecast 2021–2100, predictions compared two CMIP6 scenarios, SSP2-4.5 SSP5-8.5. hierarchical clustering results revealed that Province encompassed three homogeneous regions. Clusters H1 H2, which situated inland regions major agricultural areas, highest. SAO-CNN-BiGRU-Attention SSP5-8.5 forecasting generally displayed a monotonically growing trend period regions; model declining tendency at few points. According results, 2091–2100, H1, H3 will reach their peaks; show peak 2031–2040. At end period, for H3, rate increased by 1.31, 1.56%, 1.80%, respectively, whereas SSP2-4.5’s 0.31%, 0.95%, 1.57%, SSP5-8.5’s 10.88%, 10.76%, 10.69%, respectively. similar those (R2 > 0.96). can be used future ET0.

Язык: Английский

---

A two-stage hybrid model for dissolved oxygen prediction and control in aquaculture

Aquaculture International, Год журнала: 2025, Номер 33(1)

Опубликована: Янв. 1, 2025

Язык: Английский

---

Dissolved oxygen prediction in the Taiwan Strait with the attention-based multi-teacher knowledge distillation model

Ocean & Coastal Management, Год журнала: 2025, Номер 265, С. 107628 - 107628

Опубликована: Март 22, 2025

Язык: Английский

---

WT-DSE-LSTM: A hybrid model for the multivariate prediction of dissolved oxygen

Alexandria Engineering Journal, Год журнала: 2025, Номер 124, С. 285 - 296

Опубликована: Апрель 6, 2025

Язык: Английский

---