The use of PCA and ANN to improve evaluation of the WQIclassic, development of a new index, and prediction of WQI, Coastel Constantinois, northern coast of eastern Algeria

Abstract

The objective of this research is to arrive at a better assessment of the quality of surface water in the Constantine region. The focus is on the comparison of three classical indices WQINSF (National Sanitation Foundation Water Quality Index), WQICCME (Canadian Council of Ministers of the Environment Water Quality Index) and WQIAP (weighted arithmetical Water Quality Index), the development of a new index and the prediction by ANN (Artificial neural network) of WQI indices. The PCA allows to select 10 parameters to be used in the calculation of the classical WQI, and 8 principal components to be used as input for the new proposed index (regularized WQI). However the ANN is applied for the search of prediction models of classical WQI and developed WQI. The results show that the WQIAP index assesses water quality better, and that the regularized WQI further promotes the assessment of water quality. WQIR shows that after the pollution peak the water quality does not return to its initial state. The modeling approach by ANN offers an effective alternative to predict the WQI, it subsequently appears that the ANN predicts better the new index WQIRregularized (R2 = 0.999) than the classic model WQIAP (R2 = 0.99).

Artificial Neural Network (ANN)-Based Water Quality Index (WQI) for Assessing Spatiotemporal Trends in Surface Water Quality—A Case Study of South African River Basins

“…The second layer contains neurons responsible for processing the incoming data and transmits the findings to the third layer of neurons, which combines everything into a single aggregated output report. These computational cells are called neuro-nodes or input and output units [4,22,23].…”

“…ANNs are basic non-linear statistical algorithms used to improve artificial intelligence (AI) and address data-driven challenges that are difficult or impractical to manage using human or mathematical methods [7,8,[10][11][12]15,22,24]. ANNs are transforming conventional technologies to evaluate water quality indices, thus developing convenient analytical platforms while making water quality data readily available with little effort.…”