Parasar Prashant, Krishna Akhouri Pramod
Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India.
Sci Rep. 2025 Jul 26;15(1):27302. doi: 10.1038/s41598-025-13221-x.
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/s, Nash-Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN's robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. The explainability offered by SHAP confirms informed water resource planning. This novel framework presents a reproducible and climate-resilient decision support tool, particularly suitable for monsoon-influenced, data-limited basins susceptible to extreme hydroclimatic events.
准确的河流流量预测对于有效的水资源管理至关重要，特别是在容易出现季风变化和极端天气事件的地区。本研究提出了一个用于苏巴纳雷卡河流域（SRB）每日河流流量预测的可解释深度学习框架，将柯尔莫哥洛夫 - 阿诺德网络（KAN）与夏普利值加性解释（SHAP）相结合。利用高排放共享社会经济路径（SSP585）情景下五个耦合模式比较计划第六阶段（CMIP6）通用循环模型（GCM）的水文气候输入数据，该模型在四个活跃的测量站进行了训练和评估：穆里、阿迪蒂亚布尔、贾姆谢德布尔和加齐拉，涵盖1980年至2022年期间，并将预测延伸至2100年。本研究的主要发现如下：（1）KAN表现出较高的预测性能，各站点的均方根误差（RMSE）值在42.7至58.3米/秒之间，纳什 - 萨特克利夫效率（NSE）在0.80至0.87之间，平均绝对误差（MAE）在28.9至52.7之间，R值在0.84至0.90之间。（2）基于SHAP的特征贡献分析确定相对湿度（hurs）、比湿（huss）和温度（tas）为关键预测因子，而由于GCM降水数据在空间上存在固有不一致性，降水量（pr）的贡献有限。（3）自举SHAP分布突出了特征重要性的显著变化，特别是对于湿度变量，揭示了模型解释中各站点特定的不确定性模式。（4）KAN框架结果表明具有很强的时间一致性和物理真实性，证实了KAN在捕捉季风影响环境下的季节性流量动态和极端流量事件方面的稳健性。（5）在本研究中，带有SHAP（夏普利值加性解释）的KAN被应用于SRB等受季风影响且数据有限的地区的水文建模，与传统模型相比，提供了更高的准确性、功能精度和效率。SHAP提供的可解释性为明智的水资源规划提供了依据。这个新颖的框架提出了一个可重复且具有气候韧性的决策支持工具，特别适用于受季风影响、数据有限且易受极端水文气候事件影响的流域。
