کاربرد مدلهاي هوشمند در مديريت منابع آب
محورهای موضوعی : مدیریت منابع آب
یاسر سبزواری
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سعید اسلامیان
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1 - گروه علوم و مهندسي آب، دانشکده مهندسي کشاورزي؛ دانشگاه صنعتي اصفهان.
2 - گروه علوم و مهندسي آب، دانشکده مهندسي کشاورزي؛ دانشگاه صنعتي اصفهان.
کلید واژه: هوش مصنوعي, مدلهاي هوشمند, يادگيري عميق, اينترنت اشيا, مديريت منابع آب,
چکیده مقاله :
منابع آب، يکي از مهم‌ترين عناصر حياتي بشر و از جمله عوامل پيشرفت در مناطق مختلف به شمار مي‌رود. مديريت اين منابع مهم، امري ضروري و چالش برانگيز در دنياي امروز بوده که مولفه‌هاي مختلفي را شامل مي‌شود. امور مربوط به حفظ منابع آب، برداشت آب، برنامه‌ريزي منابع آب موجود و توزيع بسيار مناسب آن بين بخش‌هاي مختلف مصرف ازجمله اين مولفه‌ها هستند. يکي از مولفه-هاي کليدي در استفاده بهينه از منابع آب موجود، مديريت صحيح منابع آب موجود با استفاده از فناوري‌هاي پيشرفته است. مديريت آب شامل امور حفظ منابع آب، برداشت آب، برنامه‌ريزي منابع آب موجود و توزيع بسيار مناسب آن بين مصرف کنندگان است. شيوه‌هاي مديريت آب بايد به طور کامل در نظر گرفته شده تا منابع آب در دراز مدت پايدار بماند. منابع آب شيرين در دسترس بسيار محدود بوده و از اين ميزان اندک نيز تقريباً ۹۷ درصد آن، شور بوده و براي آشاميدن مناسب نيست. مشکل آلودگي، آب موجود را نيز تحت تأثير قرار مي‌دهد. بخش‌هاي مختلفي منابع آب را تحت تاثير قرار مي‌دهد، از جمله: کشاورزي، شرب، صنعت. آب از منابع مختلف بايد به شيوه‌اي کارآمد استفاده شده که در روش‌هاي مديريت سنتي آب وجود ندارد. روش‌هاي موجود براي استفاده از آب چندان مقرون به صرفه نيستند. در کنار فشار بر منابع از جانب اين بخش‌ها، تمايلي هم به اجراي آخرين فناوري‌هاي اطلاعات و ارتباطات وجود ندارد. الگوريتم‌هاي يادگيري ماشين اين پتانسيل را دارند که فرآيند يادگيري را به صورت تصاعدي با يک هدف خاص گسترش دهند. مديريت به شيوه نوين آب در زمينه‌هايي مانند کشاورزي، تامين آب، صنعت، توليد برق آبي، توليدات دامي و... مورد نياز است. از اين رو مطالعه و کاربرد روش‌هاي نوين مانند يادگيري ماشين براي بهبود مديريت منابع آب ضروري است. اين مطالعه چالش‌ها و فرصت‌هاي مختلف در رابطه با اجراي شبکه‌هاي عصبي عميق براي فرآيند مديريت آب را مورد بحث قرار داد. بنابراين، اين مطالعه، پيشنهادي در راستاي جهت‌گيري‌هاي آينده براي فعاليت‌هاي تحقيقاتي آتي در مورد چالش‌ها و مسائل اجراي مديريت آب با شبکه‌هاي عصبي عميق ارائه مي‌کند.
Abstract:
Water resources are one of the most important elements of humanity and are among the factors of progress in various regions. The management of these important resources is an essential and challenging matter in today's world that includes various components. Matters related to the conservation of water resources, water withdrawal, planning of existing water resources and their very appropriate distribution among different consumption sectors are among these components. One of the key components in the optimal use of existing water resources is the proper management of existing water resources using advanced technologies. Water management includes matters related to the conservation of water resources, water withdrawal, planning of existing water resources and their very appropriate distribution among consumers. Water management methods must be fully considered so that water resources remain sustainable in the long term. Available freshwater resources are very limited and of this small amount, approximately 97% of it is saline and not suitable for drinking. The problem of pollution also affects existing water. Various sectors affect water resources, including: agriculture, drinking, industry. Water from different sources needs to be used in an efficient manner that is not available in traditional water management methods. Existing methods for water use are not very cost-effective. Along with the pressure on resources from these sectors, there is also a lack of interest in implementing the latest information and communication technologies. Machine learning algorithms have the potential to expand the learning process exponentially with a specific goal. Modern water management is needed in fields such as agriculture, water supply, industry, hydroelectric power generation, livestock production, etc. Hence, the study and application of modern methods such as machine learning is essential for improving water resource management. This study discussed various challenges and opportunities related to the implementation of deep neural networks for the water management process. Therefore, this study provides a suggestion in the direction of future research activities on the challenges and issues of implementing water management with deep neural networks.
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