2022/10/12 by Somya Sharma, Sharma, Somya, Rahul Ghosh +13
Engineering · Environmental Science · #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Hydrology and Watershed Management Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2210.06213
openalex publication_date 2022/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow prediction. For solving inverse problems in such applications, ensuring explainability is pivotal for tackling issues relating to data bias and large search space. We propose a probabilistic inverse model framework that can reconstruct robust hydrology basin characteristics from dynamic input weather driver and streamflow response data. We address two aspects of building more explainable inverse models, uncertainty estimation and robustness. This can help improve the trust of water managers, handling of noisy data and reduce costs. We propose uncertainty based learning method that offers 6% improvement in R2 for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17% reduction in uncertainty (40% in presence of noise) and 4% higher coverage rate for basin characteristics.