Improved monthly runoff time series prediction by integrating ICCEMDAN and SWD with ELM DOI
Huifang Wang, Xuehua Zhao,

Qiucen Guo

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 5, 2024

Abstract Accurate and timely runoff prediction is a powerful basis for important measures such as water resource management flood drought control, but the stochastic of brought by environmental changes human activities poses significant challenge to obtaining reliable results. This paper develops secondary decomposition hybrid mode. In first stage model design, improved complete ensemble empirical mode with adaptive noise (ICEEMDAN) utilized discover frequencies in predicted non-stationary target data series, where inputs are decomposed into intrinsic modal functions. second stage, swarm (SWD) required decomposing high-frequency components whose time-shift multi-scale weighted permutation entropy (TSMWPE) values remain calibrated be high sub-sequences, further identifying establishing attributes that will incorporated extreme learning machine (ELM) algorithm order simulate respective series component aggregated comprehensive tool prediction. The shows superior accuracy, Nash-Sutcliffe efficiency exceeds 0.95 qualification rate greater than 0.93, which can used decision-making system design an efficient accurate generating predictions, especially hydrological problems characterized data.

Язык: Английский

Runoff prediction using a multi-scale two-phase processing hybrid model DOI
Xuehua Zhao, Huifang Wang,

Qiucen Guo

и другие.

Stochastic Environmental Research and Risk Assessment, Год журнала: 2025, Номер unknown

Опубликована: Янв. 19, 2025

Язык: Английский

Процитировано

3
Comparative analysis of correlation and causality inference in water quality problems with emphasis on TDS Karkheh River in Iran DOI Creative Commons
Reza Shakeri, Hossein Amini, Farshid Fakheri

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 22, 2025

Abstract Water quality management is a critical aspect of environmental sustainability, particularly in arid and semi-arid regions such as Iran where water scarcity compounded by degradation. This study delves into the causal relationships influencing quality, focusing on Total Dissolved Solids (TDS) primary indicator Karkheh River, southwest Iran. Utilizing comprehensive dataset spanning 50 years (1968–2018), this research integrates Machine Learning (ML) techniques to examine correlations infer causality among multiple parameters, including flow rate (Q), Sodium (Na + ), Magnesium (Mg 2+ Calcium (Ca Chloride (Cl − Sulfate (SO 4 2− Bicarbonates (HCO 3 pH. For modeling causation, “Back door linear regression” approach has been considered which establishes stable interpretable framework inference clear assumptions. Predictive was used show difference between correlation causation along with interpretability make predictive model transparent. does not report variables it showed Mg contributing target while findings reveal that TDS predominantly positive influenced Mg, Na, Cl, Ca SO , HCO pH exerting negative (inverse) effects. Unlike correlations, demonstrate directional often unequal influences, highlighting driver levels. novel application ML-based provides cost-effective time-efficient alternative traditional experimental methods. The results underscore potential ML-driven analysis guide resource policy-making. By identifying key drivers TDS, proposes targeted interventions mitigate deterioration. Moreover, insights gained lay foundation for developing early warning systems, ensuring proactive sustainable similar hydrological contexts.

Язык: Английский

Процитировано

1
A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction DOI Creative Commons
Huifang Wang, Xuehua Zhao,

Qiucen Guo

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 30, 2024

Язык: Английский

Процитировано

2
Inconsistent Monthly Runoff Prediction Models Using Mutation Tests and Machine Learning DOI

Miaomiao Ren,

Wei Sun, Shu Chen

и другие.

Water Resources Management, Год журнала: 2024, Номер 38(13), С. 5235 - 5254

Опубликована: Июнь 18, 2024

Язык: Английский

Процитировано

1
Improved monthly runoff time series prediction by integrating ICCEMDAN and SWD with ELM DOI
Huifang Wang, Xuehua Zhao,

Qiucen Guo

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 5, 2024

Abstract Accurate and timely runoff prediction is a powerful basis for important measures such as water resource management flood drought control, but the stochastic of brought by environmental changes human activities poses significant challenge to obtaining reliable results. This paper develops secondary decomposition hybrid mode. In first stage model design, improved complete ensemble empirical mode with adaptive noise (ICEEMDAN) utilized discover frequencies in predicted non-stationary target data series, where inputs are decomposed into intrinsic modal functions. second stage, swarm (SWD) required decomposing high-frequency components whose time-shift multi-scale weighted permutation entropy (TSMWPE) values remain calibrated be high sub-sequences, further identifying establishing attributes that will incorporated extreme learning machine (ELM) algorithm order simulate respective series component aggregated comprehensive tool prediction. The shows superior accuracy, Nash-Sutcliffe efficiency exceeds 0.95 qualification rate greater than 0.93, which can used decision-making system design an efficient accurate generating predictions, especially hydrological problems characterized data.

Язык: Английский

Процитировано

0