ArticleScientific reports2025
Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Article
- Dynamic monitoring of ecological security patterns in arid zone oases: a remote sensing-based ecological index evolution analysis.Scientific reports · 2025Article
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Abstract
Accurate river discharge forecasting is essential for effective water resource management, particularly in regions prone to monsoonal variability and extreme weather events. This study presents an interpretable deep learning framework for daily river discharge forecasting in the Subarnarekha river basin (SRB), integrating Kolmogorov Arnold networks (KAN) with Shapley additive exPlanations (SHAP). Leveraging hydroclimatic inputs from five coupled model intercomparison project phase 6 (CMIP6) general circulation models (GCM) under the high emissions shared socioeconomic pathway (SSP585) scenario, the model was trained and evaluated across four active gauging stations: Muri, Adityapur, Jamshedpur, and Ghatsila covering the period 1980 to 2022, with projections extending to 2100. The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m
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