Geospatial Predictive Analytics Model for Urban Impervious Surface Detection: A Study on North Central Province, Sri Lanka

Impervious surfaces change the natural hydrology due to lower levels of water infiltration, increasing stream peak flows and flood risks. Concentrated storm water runoff over the landscape can contribute to pollutants and contamination of drinking water, streams and aquifers. In the past decade (2010–2020), the North Central Province of Sri Lanka has experienced a series of anomalously severe flash flood events during annual monsoon rain from December to January. While regional paddy production has experienced successes and failures, the failures have dominated due to adverse climate conditions. This study aims to develop supervised machine learning-based geospatial analytics models to classify spatial and temporal impervious surface cover changes. Following the literature on remote sensing in conjunction with machine learning, we deploy Google Earth Engine-based machine learning algorithms under the localised climate zone (LCZ) classification workflow approach to predict the imperviousness of surfaces in the northern part of Sri Lanka during 2013–2020. The ground truth for the training data set is established via Google earth images and field survey data extracted from urban areas such as the Anuradhapura and Polonnaruwa districts of the North Central Province. Random forest (RF) and classification and regression tree (CART) classifications were used to train and test the data extracted from the Landsat imageries. CART classification gives promising results. Performance measures (F1 scores) for impervious, vegetation, water, agriculture and bare lands are 0.71, 0.96, 0.96, 0.91 and 0.91, respectively. The predictive model with a pixel density analysis conducted at the lowest level of local administrative divisions appears practically and conceptually appealing to aggregated and disaggregated urban systems.