نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی کارشناسی ارشد دانشگاه آزاد اردستان
2 عضو هئیت علمی دانشگاه آزاد اردستان
3 دانش آموخته دکتری آبیاری و زهکشی، دانشگاه زابل
چکیده
هدف از این پژوهش استفاده از الگوریتم درختی داده‌کاوی، به منظور مدیریت مناسب بر آبخوان دشت تیروان و کرون است. در این خصوص از هفت عامل مختلف انسانی و طبیعی تأثیرگذار بر تغییرات عمق آبخوان استفاده شد. در ابتدا پیشبینی سه الگوریتم درختی CART، CHAID و MP5 در تغییرات آبخوان با استفاده از شاخص‌های آماری مورد ارزیابی قرار گرفتند. الگوریتم CAHID با توجه به ضریب رگرسیون برابر 82/0 و متوسط مطلق خطا برابر 12/0 دارای عملکرد بهتری نسبت به دو الگوریتم CART و MP5 است. بیشترین مقدار بالاآمدگی آبخوان در ماه‌های آذر، دی، بهمن، اسفند و در زمانی که مقدار حجم بارندگی بین 08/0 تا 72/0 میلیون متر مکعب و درصد رطوبت هوا بیشتر از 72 درصد بوده و همچنین بیشترین مقدار افت آبخوان در ماه‌های شهریور، مرداد و در زمانی که دمای هوا بیشتر از 25 درجه سانتی‌گراد و حجم آب برداشتی از چاه‌های کشاورزی بیشتر از 32/1 میلیون مترمکعب بوده است، توسط نمودار درختی الگوریتم CHAID پیشبینی شد. از عوامل طبیعی، دمای هوا و از عوامل انسانی، حجم آب برداشتی از چاه کشاورزی بیشترین تأثیر در تغییرات عمق آبخوان در دشت مذکور داشته است. دو عامل درصد رطوبت هوا و مقدار حجم بارش، تنها عواملی بودند که رابطه مستقیم با بالاآمدگی عمق آبخوان داشته‌اند. تأثیرگذارترین عوامل در پیشبینی مقدار تغییرات عمق آبخوان دشت تیروان و کرون به ترتیب دمای هوا، حجم آب برداشتی از چاه کشاورزی و حجم بارندگی و بقیه پارامترها تقریبا تأثیرشان با هم برابر بوده است.
کلیدواژه‌ها
عنوان مقاله [English]
Investigation and Prediction of Impact of Different Factors on Aquifer Depth Change Using Tree Algorithm (Case Study: Tirvan and Carvan Plain)
نویسندگان [English]
- Negar Akbari 1
- Masoud Nasri 2
- Seyyedhassan Mirhashemi 3
1 M.Sc. student of Ardestan Azad University
2 Faculty member of Ardestan Azad University
3 Graduated ph.d, Dept. of Water Engineering, Faculty of Water and Soil, University of Zabol.
چکیده [English]
In this study, with using the data mining algorithm prediction, more efficient management of the aquifer of Tirvan and Karvan can be done. Seven different human and natural factors affecting aquifer depth changes were used in this manuscript. Initially, the predictions of three tree algorithms CART, CHAID and MP5 in aquifer changes were evaluated using statistical indices. The CAHID algorithm performed better than the CART and MP5 algorithms with respect to the regression coefficient of 0.82 and absolute error mean of 0.12. The highest aquifer rise in December, January, February and March, when the amount of precipitation was between 0.08 to 0.72 million cubic meters and the air humidity percentage was more than 72% and also the highest aquifer drawdown in month August and September, when air temperature more than 25 centigrade and the volume of water discharged from agricultural wells were more than 1.32 million cubic meters, were predicted by the CHAID algorithm tree diagram. From natural factors, air temperature and human factors, the volume of water harvested from agricultural wells had the greatest impact on aquifer depth changes in the plain. The two factors of air humidity percentage and precipitation volume were the only factors that had a direct relationship with the aquifer depth elevation. The most influential factors in predicting the depth changes of the aquifer of Tirvan and Karvan were air temperature, volume of water harvested from agricultural wells, and Precipitation volume and other parameters, respectively.
کلیدواژه‌ها [English]
- Agricultural Well
- Air Temperature
- Aquifer Drop
- CHAID Algorithm
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