Predictive Analytics through Data-Mining for Water and Environmental Management

Our research focuses on transforming vast environmental and water resources datasets into "intelligent" systems capable of predicting complex natural phenomena with high accuracy. This datamining research is characterized by the transition from traditional deterministic models to Data- Driven Heuristics. The core pillars of our data mining research include:  Intelligent Reservoir Operation: Developing fuzzy-logic, ANN-based (Artificial Neural Network), SVM based data mining models to optimize water release from reservoirs, ensuring irrigation demands are met even during stochastic drought periods.  Stochastic Hydrology: Using data mining to identify point and regional drought patterns through historical time-series analysis and Data mining.  Contaminant Transport Modelling: Applying neural networks to predict the spread of pollutants in groundwater.  Urban System Optimization: Utilizing Genetic Algorithms (GA) and data-driven methods for designing efficient urban air quality and urban water distribution systems.