مقایسه مدل‌‌‌‌‌‌‌‌های بهینه شبکه عصبی مصنوعی برای شبیه‌سازی نیترات آب زیرزمینی (مطالعه موردی: دشت بهبهان)
محورهای موضوعی : شبکه های عصبی و یادگیری عمیقعاطفه صیادی شهرکی 1 , فهیمه صیادی شهرکی 2 , بیژن حقیقتی 3
1 - سازمان
2 - دانشگاه آزاد اسلامی استان تهران واحد شهر قدس
3 - سازمان تحقیقات، آموزش و ترویج کشاورزی شهرکرد
کلید واژه: الگوریتم بهینه‌سازی تجمع ذرات, الگوریتم ژنتیک, شبکه عصبی مصنوعی, شبیه‌سازی, نیترات.,
چکیده مقاله :
آب‌های زیرزمینی در مناطق خشک و نیمه‌خشک مهم‌ترین منبع آب مورد استفاده برای مصارف شرب و کشاورزی به‌شمار می‌آیند. به همین دلیل توجه به کیفیت آن‌ها اهمیت دارد. نیترات یکی از آلوده‌کننده‌های منابع آب زیرزمینی است که توسط فعالیت‌های کشاورزی و فاضلاب تولید می‌شود. با توجه به اینکه اندازه‌گیری نیترات توسط روش‌های صحرایی و نمونه‌برداری بسیار پرهزینه و محدود است، استفاده از روش‌های نوین پیش‌بینی مانند شبکه‌های عصبی مصنوعی می‌تواند ابزار مفیدی برای این کار به شمار رود. استفاده از شبکه‌های عصبی مصنوعی در مطالعات هیدرولوژیکی دهه اخیر، نشان می‌دهد این مدل‌ها توانایی بالائی در کشف رابطه بین داده‌ها و شناخت الگوها دارند. در این پژوهش شبکه عصبی مصنوعی در ترکیب با الگوریتم PSO و نیز GA برای شبیه سازی میزان نیترات آب‌های زیرزمینی دشت بهبهان مورد ارزیابی قرار گرفته و عملکرد بسیار مناسبی را از خود نمایش می‌دهند.
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Groundwater is the most important water resource for drinking and agricultural usage especially in arid and semi-arid regions. So, it is important to note its quality. Nitrate is one of the groundwater pollutants which is mostly derived from agricultural and wastewater sources. Since nitrate determination using sampling was very expensive and limited, it is necessary to use new prediction methods like artificial neural network. The use of artificial neural networks in hydrological studies of the last decade shows that these models have a high ability to discover the relationship between data and recognize patterns. The success of neural network models in estimating different parameters of water sources has always been emphasized by different researchers. |
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