Analysis of the Spatiotemporal Patterns of Water Conservation in the Yangtze River Ecological Barrier Zone Based on the InVEST Model and SWAT-BiLSTM Model Using Fractal Theory: A Case Study of the Minjiang River Basin

Fractal and Fractional, Journal Year: 2025, Volume and Issue: 9(2), P. 116 - 116

Published: Feb. 13, 2025

The Yangtze River Basin serves as a vital ecological barrier in China, with its water conservation function playing critical role maintaining regional balance and resource security. This study takes the Minjiang (MRB) case study, employing fractal theory combination InVEST model SWAT-BiLSTM to conduct an in-depth analysis of spatiotemporal patterns conservation. research aims uncover relationship between dynamics watershed capacity ecosystem service functions, providing scientific basis for protection management. Firstly, is introduced quantify complexity spatial heterogeneity natural factors such terrain, vegetation, precipitation Basin. Using model, evaluates functions area, identifying key zones their variations. Additionally, employed simulate hydrological processes basin, particularly impact nonlinear meteorological variables on responses, aiming enhance accuracy reliability predictions. At annual scale, it achieved NSE R2 values 0.85 during calibration 0.90 validation. seasonal these increased 0.91 0.93, at monthly reached 0.94 0.93. showed low errors (RMSE, RSR, RB). findings indicate significant differences Basin, upper middle mountainous regions serving primary areas, whereas downstream plains exhibit relatively lower capacity. Precipitation, terrain slope, vegetation cover are identified main affecting changes having notable regulatory effect Fractal dimension reveals distinct structure which partially explains geographical distribution characteristics functions. Furthermore, simulation results based show increasingly climate change human activities frequent occurrence extreme events, particular, disrupts posing greater challenges Model validation demonstrates that SWAT integrated BiLSTM achieves high capturing complex processes, thereby better supporting decision-makers formulating management strategies.

Language: Английский

Data-driven identification of pollution sources and water quality prediction using Apriori and LSTM models: A case study in the Hanjiang River basin

Journal of Contaminant Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 104570 - 104570

Published: April 1, 2025

Language: Английский

Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning

Environments, Journal Year: 2025, Volume and Issue: 12(3), P. 94 - 94

Published: March 17, 2025

The increasing frequency and severity of hydrological extremes due to climate change necessitate accurate baseflow estimation effective watershed management for sustainable water resource use. Soil Water Assessment Tool (SWAT) is widely utilized modeling but shows limitations in simulation its uniform application the alpha factor across Hydrologic Response Units (HRUs), neglecting spatial temporal variability. To address these challenges, this study integrated SWAT with Tree-Based Pipeline Optimization (TPOT), an automated machine learning (AutoML) framework, predict HRU-specific factors. Furthermore, a user-friendly web-based program was developed improve accessibility practical optimized factors, supporting more predictions, even ungauged watersheds. proposed approach area significantly enhanced recession predictions compared traditional method. This improvement supported by key performance metrics, including Nash–Sutcliffe Efficiency (NSE), coefficient determination (R2), percent bias (PBIAS), mean absolute percentage error (MAPE). framework effectively improves accuracy practicality modeling, offering scalable innovative solutions face stress.

Language: Английский