دانلود مقاله بررسی کاربرد یادگیری ماشین در ارزیابی کیفیت آب
خلاصه
1. مقدمه
2. مروری بر یادگیری ماشین
3. کاربرد یادگیری ماشین برای محیط های مختلف آبی
4. سخنان پایانی
اعلامیه منافع رقیب
قدردانی ها
منابع
Abstract
1. Introduction
2. Overview of machine learning
3. Application of machine learning for different water environments
4. Concluding remarks
Declaration of competing interests
Acknowledgments
References
چکیده
با افزایش سریع حجم داده ها در محیط آبی، یادگیری ماشین به ابزار مهمی برای تجزیه و تحلیل، طبقه بندی و پیش بینی داده ها تبدیل شده است. برخلاف مدل‌های سنتی مورد استفاده در تحقیقات مرتبط با آب، مدل‌های مبتنی بر داده‌های مبتنی بر یادگیری ماشینی می‌توانند به طور موثر مسائل غیرخطی پیچیده‌تری را حل کنند. در تحقیقات محیط آب، مدل‌ها و نتایج حاصل از یادگیری ماشینی برای ساخت، نظارت، شبیه‌سازی، ارزیابی و بهینه‌سازی سیستم‌های مختلف تصفیه و مدیریت آب استفاده شده‌اند. علاوه بر این، یادگیری ماشینی می تواند راه حل هایی برای کنترل آلودگی آب، بهبود کیفیت آب و مدیریت امنیت اکوسیستم حوزه آبخیز ارائه دهد. در این بررسی، مواردی را شرح می‌دهیم که در آن الگوریتم‌های یادگیری ماشین برای ارزیابی کیفیت آب در محیط‌های مختلف آبی مانند آب‌های سطحی، زیرزمینی، آب آشامیدنی، فاضلاب و آب دریا استفاده شده‌اند. علاوه بر این، ما کاربردهای احتمالی آینده رویکردهای یادگیری ماشین را برای محیط‌های آبی پیشنهاد می‌کنیم.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.
Introduction
With rapid economic development, wastewater containing various pollutants is generated, posing serious threats to natural water environments. Thus, various water pollution control measures have been developed. To a large extent, water quality analysis and evaluation have substantially improved the efficiency of water pollution control [1]. To date, many methods have been developed to monitor and assess water quality worldwide, such as the multivariate statistical method [2], fuzzy inference [3], and the water quality index (WQI) [4]. For evaluating water quality, although most water quality parameters can be monitored according to the procedures defined in the relevant standards, the final water quality evaluation results may widely vary owing to the choice of parameters [5]. Considering all water quality parameters is unrealistic because it is not only expensive and technically difficult but also fails to deal with the variability in water quality [6]. However, in recent years, with the advances in machine learning methods, an increasing number of researchers believe that vast amounts of data can be successfully captured and analyzed to meet the complex and large-scale water quality evaluation requirements.
Concluding remarks
Machine learning has been widely used as a powerful tool to solve problems in the water environment because it can be applied to predict water quality, optimize water resource allocation, manage water resource shortages, etc. Despite this, several challenges remain in fully applying machine learning approaches in this field to evaluate water quality: (1) Machine learning is usually dependent on large amounts of high-quality data. Obtaining sufficient data with high accuracy in water treatment and management systems is often difficult owing to the cost or technology limitations. (2) As the conditions in real water treatment and management systems can be extremely complex, the current algorithms may only be applied to specific systems, which hinders the wide application of machine learning approaches. (3) The implementation of machine learning algorithms in practical applications requires researchers to have certain professional background knowledge.