AI-Enabled Geospatial Time-Series Analysis For Smart River Water Quality Management Toward Sustainable Cities

Abstract

The water quality degradation of urban rivers due to population pressure, rapid industrialization, change of land use, and climate variability has become serious concern throughout the world. Traditional water quality monitoring system based on laboratory sampling and analysis is not enough for continuous assessment and surveillance because of its limitation of high cost, labor intension, and low spatial coverage. Everyday high resolutions earth observation (EO), artificial intelligent (AI), geographic information system (GIS), and cloud computing technologies can offer enormous opportunities to construct intelligent multisource time-series monitoring setup for supporting the sustainable management of urban water bodies. This paper proposes an integrated AI enabled spatialtemporal water quality monitoring structure supported by multi-source GIS and EO data sets for smart river water quality management using the case of Mula-Mutha River Pune India. The setup interfacss the temporal series RiverWatch monitoring data of eight stations (2017–2023) with the global EO data sets (Sentinel-2 MSI and Landsat-8), which are processed using cloud platform Google Earth Engine (GEE) and desktop applications ArcGIS Pro. River water quality indicators, such as pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Total Coliform, and Fecal Coliform, are integrated with other externally derived from remotely sensed indices, such as Normalized difference water index (NDWI), Normalized difference turbidity index (NDTI), and Normalized difference chlorophyll index (NDCI). Also missing data due to observation gaps are treated with strict time-respect linear-interpolation strategy developed in previous work, which have offered reliable data basis for the AI enabled model development. The future weekly river water quality status is forecasted using emerging AI models, like long short term memory (LSTM), Random Forest (RF), Support vector regression (SVR), and Gradient Boosting (GB). The predicted data are then fed into integrated with GIS-based decision support dashboard for dynamic real-time visualization, hotspot based analysis, seasonal trend detection, and environmental health risk assessment. The case of the integrated AI-enabled water quality monitoring system can not only assist local municipal authorities in planning and enforcement of pollution-control measures for urban rivers but also contribute to support nationally important SDG6 (Clean water and sanitation), and SDG11 (Sustainable cities and communities).