Environmental Industry Letters https://journals.tultech.eu/index.php/eil <p style="font-weight: 400;"><strong>Environmental Industry Letters (EIL)</strong> is an innovative open-access journal that sits at the intersection of environmental science and industrial development. Our mission is to offer a unique perspective on the mutually beneficial relationship between industries and the environment and, hence, promote practical and sustainable approaches to their coexistence. EIL maintains a global perspective, addressing the diverse needs and challenges faced by industries across developed and developing nations. The journal distinguishes itself by introducing fresh viewpoints into the scientific discourse, enriching our understanding of the intricate connection between industry and the environment.</p> <p style="font-weight: 400;"> </p> en-US Maliheh.Arab@tultech.eu (Associate Editor: Ms. Maliheh Arab) eil.tultech@gmail.com (Administrator) Wed, 30 Oct 2024 19:53:57 +0100 OJS 3.3.0.15 http://blogs.law.harvard.edu/tech/rss 60 A Comparative Analysis of Rainfall-Prediction Using Optimized Machine Learning Algorithms https://journals.tultech.eu/index.php/eil/article/view/219 <p>The difficult challenge of predicting rainfall is brought on by the daily observations of erratic rainfall patterns and climatic fluctuations. Predicting when the rain will fall can help avoid floods and even aid in crop growth in agriculture. Timely and precise predictions can prevent loss of life and assets. The ability to forecast the amount of rainfall requires an understanding of weather-related elements such as pressure, humidity, wind speed, latitude, longitude, and precipitable water with varying x and y-axis parameters. The research in this study involves using fundamental machine learning techniques to create weather forecasting models that use the day's meteorological data to predict whether or not it will rain tomorrow. By utilizing previously identified trends from historical meteorological data, machine learning helps forecast rainfall. We are using a classification model in our supervised data model, and the techniques utilized to forecast the amount of rain include random forest, KNN, decision tree, and logistic regression. Using machine learning algorithms to examine past weather data and find patterns that can be applied to forecast future rainfall patterns is the suggested technique for rainfall prediction. A more accurate weather forecast is made possible by all of the aforementioned factors. We will handle the information that aids in eliminating erroneous and incomplete data. As a component of data preparation, normalization helps to improve feature approximation by adjusting the range of independent variables. Model training is carried out following data preparation, during which data is divided into training and test sets. The test set aids in prediction-making, while the training set serves as the foundation for model training.</p> S Sindhura, P Dedeepya, N Sampreet Chowdary, Katragadda Megha Shyam, Balamurali Krishna Thati Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0 https://journals.tultech.eu/index.php/eil/article/view/219 Sun, 15 Sep 2024 00:00:00 +0200 The Application of Artificial Intelligence (AI) in Adsorption Process of Heavy Metals: A Systematic Review https://journals.tultech.eu/index.php/eil/article/view/119 <p>The application of Artificial Intelligence (AI) has shown significant promise in optimizing adsorption processes for heavy metal removal, an essential component of water treatment plant (WTP) operations. This systematic review presents a comprehensive analysis of AI techniques applied to improve adsorption performance, focusing on machine learning (ML) and metaheuristic algorithms. AI models, such as neural networks and support vector machines, have been leveraged to analyze large datasets related to adsorption parameters, enhancing prediction accuracy and optimizing operational efficiency. Additionally, metaheuristic algorithms like Genetic Algorithms and Simulated Annealing contribute to efficient solution exploration, identifying optimal parameter configurations for the adsorption process. The integration of AI enables real-time monitoring, predictive maintenance, and dynamic adjustment of process parameters, thus ensuring the continuous improvement of adsorption efficiency. AI-based approaches also facilitate the identification of key adsorption features, allowing for precise control and improved resource utilization. Moreover, by combining AI with traditional adsorption models, such as Langmuir and Freundlich isotherms, this review explores new methods for improving adsorption kinetics and thermodynamics. The structured implementation of AI is demonstrated as a path forward in achieving sustainable, adaptive, and reliable solutions for water quality control. Future studies should prioritize the development of more advanced AI-driven predictive systems, enhancing the applicability of these methods across different adsorption contexts and pollutant types. This review underscores the transformative potential of AI in advancing adsorption technology, paving the way for smarter water treatment solutions that enhance environmental sustainability.</p> Mohammad Gheibi, Seyyed Roohollah Masoomi, Mahad Uzairu Magala, Amir M. Fathollahi-Fard, Adel Ghazikhani, Kourosh Behzadian Copyright (c) 2024 Mohammad Gheibi, Seyyed Roohollah Masoomi, Mahad Uzairu Magala, Amir M. Fathollahi-Fard, Adel Ghazikhani, Kourosh Behzadian https://creativecommons.org/licenses/by/4.0 https://journals.tultech.eu/index.php/eil/article/view/119 Tue, 26 Nov 2024 00:00:00 +0100