Utilizing LSTM-GRU for IOT-Based Water Level Prediction Using Multi-Variable Rainfall Time Series Data DOI Creative Commons
Indrastanti Ratna Widiasari, Rissal Efendi

Informatics, Journal Year: 2024, Volume and Issue: 11(4), P. 73 - 73

Published: Oct. 8, 2024

This research describes experiments using LSTM, GRU models, and a combination of both to predict floods in Semarang based on time series data. The results show that the LSTM model is superior capturing long-term dependencies, while better processing short-term patterns. By combining strengths this hybrid approach achieves accuracy robustness flood prediction. LSTM-GRU outperforms individual providing more reliable prediction framework. performance improvement due complementary handling various aspects These findings emphasize potential advanced neural network models addressing complex environmental challenges, paving way for effective management strategies Semarang. graph GRU, scenarios shows significant differences predicting river water levels rainfall input. MAPE, MSE, RMSE, MAD metrics are presented training validation data six scenarios. Overall, provide good when complete input variables, namely, downstream upstream rainfall, compared only rainfall.

Language: Английский

Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters DOI
Majid Niazkar, Reza Piraei, Mohammad Reza Goodarzi

et al.

Environmental Processes, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 11, 2025

Language: Английский

Citations

4
Unveiling the Hidden Connections: Using Explainable Artificial Intelligence to Assess Water Quality Criteria in Nine Giant Rivers DOI
Sourav Kundu,

P. K. Datta,

Puja Pal

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144861 - 144861

Published: Jan. 1, 2025

Language: Английский

Citations

1
Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach DOI Creative Commons

Qiulin Li,

Jinchao He,

Dewei Mu

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1471 - 1471

Published: Jan. 31, 2025

Dissolved oxygen (DO) is a vital water quality index influencing biological processes in aquatic environments. Accurate modeling of DO levels crucial for maintaining ecosystem health and managing freshwater resources. To this end, the present study contributes Bayesian-optimized explainable machine learning (ML) model to reveal dynamics predict concentrations. Three ML models, support vector regression (SVR), tree (RT), boosting ensemble, coupled with Bayesian optimization (BO), are employed estimate Mississippi River. It concluded that BO-SVR outperforms others, achieving coefficient determination (CD) 0.97 minimal error metrics (root mean square = 0.395 mg/L, absolute 0.303 mg/L). Shapley Additive Explanation (SHAP) analysis identifies temperature, discharge, gage height as most dominant factors affecting levels. Sensitivity confirms robustness models under varying input conditions. With perturbations from 5% 30%, temperature sensitivity ranges 1.0% 6.1%, discharge 0.9% 5.2%, 0.8% 5.0%. Although experience reduced accuracy extended prediction horizons, they still achieve satisfactory results (CD > 0.75) forecasting periods up 30 days. The established also exhibit higher than many prior approaches. This highlights potential BO-optimized reliable forecasting, offering valuable insights resource management.

Language: Английский

Citations

0
Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP) DOI
Khaled Merabet, Fabio Di Nunno, Francesco Granata

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 27, 2025

Language: Английский

Citations

0
Estimation of suspended sediment load utilizing a super-optimized deep learning approach informed by the red fox optimization algorithm DOI
Mohammad Mahdi Malekpour, Mohammad Mehdi Ahmadi, Marcello Gugliotta

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 24, 2025

Language: Английский

Citations

0
A novel interpretable hybrid model for multi-step ahead dissolved oxygen forecasting in the Mississippi River basin DOI

Hassan M. Alwan,

Mehdi Mohammadi Ghaleni, Mahnoosh Moghaddasi

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 28, 2024

Language: Английский

Citations

0
Simulation and explanatory analysis of dissolved oxygen dynamics in Lake Ulansuhai, China DOI Creative Commons
Fan Zhang, Xiaohong Shi, Shengnan Zhao

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 57, P. 102109 - 102109

Published: Dec. 9, 2024

Language: Английский

Citations

0
Utilizing LSTM-GRU for IOT-Based Water Level Prediction Using Multi-Variable Rainfall Time Series Data DOI Creative Commons
Indrastanti Ratna Widiasari, Rissal Efendi

Informatics, Journal Year: 2024, Volume and Issue: 11(4), P. 73 - 73

Published: Oct. 8, 2024

This research describes experiments using LSTM, GRU models, and a combination of both to predict floods in Semarang based on time series data. The results show that the LSTM model is superior capturing long-term dependencies, while better processing short-term patterns. By combining strengths this hybrid approach achieves accuracy robustness flood prediction. LSTM-GRU outperforms individual providing more reliable prediction framework. performance improvement due complementary handling various aspects These findings emphasize potential advanced neural network models addressing complex environmental challenges, paving way for effective management strategies Semarang. graph GRU, scenarios shows significant differences predicting river water levels rainfall input. MAPE, MSE, RMSE, MAD metrics are presented training validation data six scenarios. Overall, provide good when complete input variables, namely, downstream upstream rainfall, compared only rainfall.

Language: Английский

Citations

0