Flexible user interface for machine learning techniques to enhance the complex geospatial hydro-climatic models with future perspective

Hydro-climatic (HC) models have complex environments due to the integration of hydrological processes and climate indices for the assessment of historical and future scenarios. The approximation of HC models leads to a major uncertainty in the selection of optimal methods for processing, enhancement, and assessment. The present work developed a User-Friendly Interface (UI) in the R programming platform to enhance the geospatial HC models using machine learning concepts. Here, UI complies with various technologies together to perform consistently with input control, processing, and visualization. To validate this interface, a snow-dominated alpine watershed was selected. The results showed that, (a) UI assisted to downscale of the future climatic data into finer resolution, (b) boosted the efficiency of the geospatial model by adaptive random forest regression with NSE = 0.92 and 0.84, respectively. Moreover, UI designed to apply for different geospatial optimization problems which assist academicians, scientists, decision-makers, planners, and stakeholders, etc.