Spatio-Temporal Pollution Forecasting using Hybrid Networks

Rising real estate prices along with expensive maintenance costs, and lack of spares during times of instrument failure have become major issues for statutory bodies when dealing with real-time pollution monitoring stations. As a possible solution to these problems, a novel class of hybrid spatio-temporal pollution forecasting networks which are a combination of various widely used temporal forecasting methods and spatial interpolation methods have been proposed in this paper. In addition, a novel multi-site Multi Layer Perception based Ensemble method, capable of improving accuracy by taking exogenous variables into account, has also been proposed. Experimental results based on the multi-site air pollution data of Beijing demonstrate that the proposed class of hybrid networks have been effective in predicting the pollution of unknown locations with great levels of accuracy. Moreover, the proposed novel MLP Ensemble method for spatial interpolation has also been empirically shown to perform equivalently in comparison to commonly used spatial interpolation methods.

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson's correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

Spatial interpolation methods for estimating monthly rainfall distribution in Thailand

Spatial interpolation methods usually differ in their underlying mathematical concepts, each with inherent advantages and drawbacks depending on the properties of data. This paper, therefore, aims to compare and evaluate the performances of well-established interpolation techniques for estimating monthly rainfall data in Thailand. The selected methods include the inverse distance-based method, multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK). The technique of searching nearest stations is additionally imposed for some aforementioned schemes. The k -fold cross-validation method is exploited to assess the efficiency of each method, then the metric scores, RMSE, and MAE are used for comparisons. The results suggest the ANN might be the least favorite as it underperforms in many folds. While the OK method provides the most accurate prediction, the inverse distance weighting (IDW), particularly inverse exponential weighting (IEW), and MLR are considerably comparative. Overall, IEW is plausible for monthly rainfall estimation of Thailand because it is less computationally expensive than the OK and its flexible computation.