مزهر عبد الحسن نجم العمار

Environmental risks have become a primary threat to the ecosystem and human health, and identifying the most influential factors is a crucial issue. Technological advancements have made it possible to identify and detect these factors, most notably artificial intelligence (AI), which has played an effective role in analysis, detection, and inference. IoT, sensors, and AI technology are highly effective in environmental monitoring, such as detecting water, air, and soil pollutants. Therefore, this review aims to present and explore the most important recent developments in the use of sensors, AI technologies, and the Internet of Things in monitoring and controlling environmental pollution, while considering the complexities of the prediction process and the dynamic nature of the environment, taking into account the variables of pollution type. However, they also present challenges. Key considerations include understanding and selecting the most appropriate AI models, the ability to adjust and balance model performance, data processing, and the problems associated with their sharing, and finally, the interpretability of results. This study aims to monitor environmental pollution by leveraging artificial intelligence and its technologies, sensors, IoT, the latest trends, and the findings of recent studies

Environmental improvement steps are important factors in relation to technological progress in the world, and it is considered one of the fierce enemies of the environment, one of its tools is carbon dioxide (CO2 ) emissions as it is an indicator of environmental pollution with its high rates in the atmosphere, and predicting its emissions and future expectations contribute greatly to drawing up a sustainable environmental policy. On this basis, this study used one of the most important artificial intelligence (AI) tools, which is the multilayer nonlinear autoregressive exogenous model (ML-NARX), to predict and forecast annual CO2 emissions and their quantities in Iraq until 2032. The measured CO2 emissions data were collected for the purpose of studying from the «Our World in Data» website from 1950 to 2022 because some previous measurements were missing, as the database consisted of 73 samples that were divided into a training set, a test set, and a validation set at a ratio of 70:15:15, respectively. The proposed model was designed to consist of two hidden layers and the effect of modifying the number of nodes in each of the two hidden layers on the accuracy of the prediction was studied. As for the inputs used, one set was formed to predict the next value from the past value based on the time sequence of CO2 emission years. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were adopted as performance measures in evaluating the model, as the network was trained to reach the lowest value of MSE and RMSE which were 6.46E-04 and 2.54E-02 respectively, and the best structure used for the model was determined to be (10) nodes in first layer and (25) nodes in second layer. The output of the model’s prediction curve for ten years for CO2 emission for the period from 2023 to 2032 showed an increase at a rate of 1.7820 million tons/year. Therefore, the results of this work will help local government and decision-makers to take the necessary and wise measures to address the environmental reality for the near and distant future as well. Keywords: prediction, ML-NARX, CO2 emission, time series.