In regions with sparse monitoring data, machine learning can be effectively utilized by leveraging techniques such as transfer learning and broad-scale modeling. Transfer learning allows models trained on data-rich source domains to be fine-tuned on the target domain with limited data, improving predictive accuracy. This approach is particularly useful when there are pre-existing models from related tasks or regions that can be adapted to the specific characteristics of the target region. By transferring knowledge from a known task to an unknown one, transfer learning helps in overcoming data scarcity issues and enhancing model performance in unmonitored sites.
Broad-scale modeling, on the other hand, involves building models using all available entities or a subgroup of relevant entities across different regions. These models incorporate site-specific characteristics and dynamic features to differentiate between various system behaviors based on geographical and environmental factors. Broad-scale models have shown success in predicting hydrological variables like streamflow and water quality in unmonitored basins by capturing heterogeneity across multiple sites.
By combining both transfer learning for localized adaptation and broad-scale modeling for comprehensive coverage of diverse entities, machine learning can effectively address challenges posed by sparse monitoring data in regions with inadequate environmental observations.