METHOD FOR FORECASTING POLLUTION LEVELS

Abstract

ABSTRACT A method (100) for forecasting pollution levels, the method (100) comprising the steps of applying an artificial bee colony optimization protocol to model pollution forecast parameters, utilizing a differential quadrature method with exponential B-spline functions to approximate numerical solutions for Burgers’ equation in the simulation of pollution dispersal using the pollution forecast parameters and calculating pollution forecasts by iterating the artificial bee colony optimization protocol.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of pollution and in particular relates to a method for forecasting pollution levels.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Forecasting pollution levels is essential in urban and industrial settings, where accurate air quality predictions can support decision-making for public health, regulatory compliance, and environmental protection.
[0004] Conventional pollution forecasting methods often rely on deterministic or statistica , Claims:1. A method (100) for forecasting pollution levels, the method (100) comprising the steps of:
applying an artificial bee colony optimization protocol to model pollution forecast parameters;
utilizing a differential quadrature method with exponential B-spline functions to approximate numerical solutions for Burgers' equation in the simulation of pollution dispersal using the pollution forecast parameters; and
calculating pollution forecasts by iterating the artificial bee colony optimization protocol.

2. The method (100) as claimed in claim 1, wherein the artificial bee colony optimization protocol is configured to modify a fitness function for improved prediction accuracy by adjusting the pollution forecast parameters based on historical pollution data and current environmental parameters.

3. The method (100) as claimed in claim 2, wherein the current environmental parameters include temperature, humidity, and wind speed.

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