Abstract
The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters.
Highlights
The present work proposes a method to model the particulate matter concentrations using the Bacterial Foraging Optimization Algorithm (BFOA); this method is considered as a novel method since it has not been found in the literature an application of the BFO algorithm in the problem of modeling the concentration of particulate material
In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA)
Once the complexity of the problem is established, we can state that the purpose of using the BFOA optimization method is the reduction of the error that exists when applying the model created with ANFIS, which is why the experiments conducted are aimed at testing the efficiency of the BFO algorithm and the different configurations of its parameters
Summary
The present work proposes a method to model the particulate matter concentrations using the BFOA; this method is considered as a novel method since it has not been found in the literature an application of the BFO algorithm in the problem of modeling the concentration of particulate material. Likewise, another contribution of the present work is to demonstrate how the adjustment of the parameters of the algorithm affects the result and the way in which each of these. Once the model optimized with BFOA is generated, it will be compared against that generated with ANFIS
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