Space–time forecasting of groundwater level using a hybrid soft computing model

摘要: ABSTRACTForecasting of space–time groundwater level is important for sparsely monitored regions. Time series analysis using soft computing tools powerful in temporal data analysis. Classical geostatistical methods provide the best estimates spatial data. In present work a hybrid framework forecasting proposed by combining tool and model. Three time models: artificial neural network, least square support vector machine genetic programming (GP), are individually combined with ordinary kriging The experimental variogram thus obtained fits linear combination nugget effect model power efficacy models was decided on both visual interpretation (spatial maps) calculated error statistics. It found that GP–kriging gave most satisfactory results terms average absolute relative error, root mean no...