پارامترهای هواشناسی اثرگذار بر مصرف آب در بخش خانگی شهر قم

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناس ارشد آمار، شرکت آب و فاضلاب شهری استان قم

2 کارشناس ارشد آمار اقتصادی و اجتماعی، دانشکده علوم ریاضی و کامپیوتر، دانشگاه شهید چمران اهواز

چکیده

پیش‌بینی مصرف آب و شناسایی عوامل مؤثر بر آن، از گام‌های مهم در مدیریت بحران آب است. پژوهش‌های انجام شده نشان می‌دهد پارامترهای هواشناسی به عنوان مهم‌ترین گروه در زمینه برآورد کوتاه مدت مصرف آب در نظر گرفته شده‌اند. در این پژوهش با استفاده از روش رگرسیون وارون قطعه‌ای خوشه‌بندی شده به شناسایی متغیرهای هواشناسی اثرگذار بر مصرف آب خانگی شهر قم پرداخته شد. با استفاده از این روش علاوه بر کاهش بعد، می‌توان مسئله همخطی را نیز رفع نمود. داده‌ها شامل هفت پارامتر هواشناسی و مصرف آب خانگی ماهانه طی سال‌های 1380 تا 1392 بود. تحلیل داده‌ها نشان داد که می‌توان به جای هفت متغیر اولیه تنها از دو مولفه جدید که ترکیبی خطی از متغیرهای مستقل هستند، استفاده نمود. در مؤلفه اول حداکثر سرعت وزش باد و رطوبت نسبی دارای بار منفی (757/0 و 4/0) و در مؤلفه دوم میانگین حداقل دما دارای بار منفی (753/0) و متوسط دمای هوا با بار مثبت (634/0) بیشترین تأثیر را بر مؤلفه‌ها داشتند. نتایج رگرسیون روی این متغیرها، معنیداری حداقل میانگین دما با ضریب 018/0 و حداکثر سرعت وزش باد با ضریب 004/0- و ضریب تعیین 92 درصد را نشان داد. مقایسه روش پیشنهادی در این پژوهش با روش معمول آنالیز مؤلفه اصلی در تحلیل داده‌های چند متغیره، نشان از خطای کمتر رگرسیون وارون قطعه‌ای خوشه‌بندی شده دارد و همچنین با توجه به تأثیر همخطی بر نتایج شبکه عصبی، روش ارائه شده عملکرد بهتری نسبت به روش‌های معمول پیش‌بینی مصرف آب دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Identification of Meteorological Parameters Affecting Water Consumption in Household Sector of Qom

نویسندگان [English]

  • Ghasem Amini 1
  • Zohre Saiedi 2
1 MSc. of Statistics, Water and Wastewater Co., Qom Province, Qom, Iran
2 MSc. of Economical & Social Statistis, Department of Math and Computer, Shahid Chamran University Ahvaz, Iran
چکیده [English]

Prediction of water consumption and its effective factors is an important step in water crisis management. Studies showed that meteorological parameters are considered as the most important factor for short-term prediction of water consumption. In this research, cluster-based sliced inverse regression method was used to identify the meteorological variables affecting the household water consumption in Qom. In addition to dimension reduction, this method can be used to remove collinearity. The data consisted of seven meteorological parameters and monthly household water consumption from 2001 to 2013. Data analysis indicated that instead of seven primary variables, only two new components which are linear combinations of independent variables can be used. The negatively charged maximum wind speed and relative humidity (0.757 and 0.4) of the first component, and the negatively charged average minimum temperature (0.753) and positively charged average air temperature (0.634) of the second component had the greatest impact on the components. The regression analysis indicated that the average minimum temperature coefficient 0.018, the maximum wind speed coefficient -0.004, and determination coefficient 0.92% are significant. Comparing the method proposed in this paper with the usual method of principal component analysis (PCA) for multivariate data analysis indicated that cluster-based sliced inverse regression has fewer errors. Moreover, noticing the impact of collinearity on the outputs of neural networks, the method proposed in this paper had better performance than the usual methods and consequently predicts water consumption.

کلیدواژه‌ها [English]

  • Dimension Reduction
  • Sliced Inversed Regression
  • Clustering
  • Collinearity
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