پیش بینی ماهیت حریق مبتنی بر یادگیری ماشین: رگرسیون لجستیک یک الگوریتم تفسیر پذیر | ||
| پژوهش های نظری و کاربردی هوش ماشینی | ||
| مقاله 8، دوره 2، شماره 1، شهریور 1403، صفحه 104-119 اصل مقاله (1.2 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/abmir.2025.22313.1068 | ||
| نویسندگان | ||
| فاطمه مسلمان زاده1؛ حمیدرضا کوشا2؛ کاظم صاعدی* 3 | ||
| 1کارشناس تحول و نوسازی سازمان آتش نشانی و خدمات ایمنی شهرداری مشهد، مشهد، ایران | ||
| 2استادیار مهندسی صنایع، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| 3شهرداری مشهد، سازمان آتش نشانی و خدمات ایمنی، مشهد، ایران | ||
| چکیده | ||
| یکی از وظایف سازمان‌های آتش‌نشانی ارائه گزارش حریق و حوادث به مراجع قضائی، بیمه و سایر نهادهای درخواست‌کننده جهت تصمیم‌گیری و پرداخت خسارت است، لذا یافتن ماهیت حریق به‌نحوی‌که مؤلفه‌های غیرعملیاتی در تصمیم کارشناسان آتش‌نشانی کمترین تأثیر را داشته باشد، اهمیت این پژوهش را بیشتر خواهد نمود. باتوجه‌به اینکه حدود 1 درصد از گزارشات حریق این سازمان ماهیت نامعلوم دارند، این موضوع باعث سردرگمی در ارائه خدمات مناسب به ارباب رجوع را داشته و تصمیم گیری با مشکل مواجه شده است. هدف از این پژوهش پیش بینی ماهیت حریق مبتنی بر الگوریتم های یادگیری ماشین در شهر مشهد می باشد. در این پژوهش ابتدا مجموعه داده حریق 7 ساله (1395-1401) مورد بررسی و واکاوی قرار گرفت و پس از آن با توجه به مسئله و ادبیات موضوع و با انجام پیش پردازش و مهندسی ویژگی مجموعه داده ای با تعداد 46 ویژگی و 28930 نمونه تهیه شد. در مرحله بعد برای پیش بینی ماهیت حریق از سه الگوریتم یادگیری ماشین با ناظر استفاده شد و نتایج آن ها با هم مقایسه شد که الگوریتم رگرسیون لجستیک با 79.66 درصد دقت با زمان اجرای 1 ثانیه نتیجه بهتری را بین سه الگوریتم جهت پیش بینی ماهیت حریق ایجاد نموده است. | ||
| کلیدواژه‌ها | ||
| ماهیت حریق؛ عمدی؛ غیرعمدی؛ یادگیری ماشین؛ رگرسیون لجستیک | ||
| عنوان مقاله [English] | ||
| Predicting the nature of fire based on machine learning: Logistic regression is an interpretable algorithm | ||
| نویسندگان [English] | ||
| Fatemeh Mosalmanzadeh1؛ Hamidreza Koosha2؛ Kazem Saedi3 | ||
| 1Organizational modernization and transformation expert, Fire&Safety Services Department, Mashhad municipality | ||
| 2Ferdowsi University of Mashhad, Sharif University of Technology, Tarbiat Modares University | ||
| 3Mashhad Municipality, Fire and Services department, Mashhad, Iran | ||
| چکیده [English] | ||
| One of the duties of firefighting organizations is to submit fire and accident reports to judicial authorities, insurance, and other requesting institutions to make decisions and pay damages, therefore, finding the nature of the fire in such a way that non-operational components have the least impact on the decision of firefighting experts increases the importance of this research. Considering that about 1% of the fire reports of this organization are unknown, this issue has confused providing proper services to Arbab-Rojoo and decision-making has been difficult. This research aims to predict the nature of fire based on machine learning algorithms in the city of Mashhad. In this research, the 7-year fire data set (1395-1401) was first examined and analyzed, and then, according to the problem and the literature, a data set with 46 features and 28930 samples was prepared by pre-processing and feature engineering. In the next step, to predict the nature of the fire, three machine learning algorithms were used with the observer and their results were compared. The logistic regression algorithm, with 79.66% accuracy and an execution time of 1 second, created a better result between the three algorithms in predicting the nature of the fire. | ||
| کلیدواژه‌ها [English] | ||
| Nature of fire, intentional, unintentional, machine learning, logistic regression | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 115 تعداد دریافت فایل اصل مقاله: 74 |
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