نوع مقاله : مقاله پژوهشی
نویسندگان
1
استادیار، گروه مهندسی عمران، مرکز تحقیقات مواد و انرژی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران.
2
دانشیار، گروه مهندسی عمران، دانشگاه آزاد اسلامی واحد خرم آباد، لرستان، ایران
3
دکترای علوم ومهندسی آب، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان لرستان، سازمان تحقیقات،
10.22077/jaaq.2025.9841.1119
چکیده
در سالهای اخیر، گرم شدن هوا، نوسانات اقلیمی و فشار برداشت از منابع آب، سبب افت سطح آبهای زیرزمینی شده است؛ بنابراین برای جلوگیری از تشدید روند فوق و مدیریت بهینه بهرهبرداری از منابع آب زیرزمینی دشتها، شبیهسازی و پیشبینی تراز آبهای زیرزمینی امری ضروری و اجتناب ناپذیر است. بهمنظور پیشبینی تغییرات سطح آب زیرزمینی آبخوان کوهدشت ابتدا از مدل گردش عمومی جو CIMP6 تحت سناریو مختلف، پارامترهای هواشناسی پیشبینی و تحلیل گردید. سپس با استفاده از پارامترهای دما، بارش و مقدار برداشت از آبخوان طی دوره آماری 2022-2002 عملکرد مدلهای هیبریدی شبکه عصبی مصنوعی- موجک(WANN) و شبکه عصبی مصنوعی - ازدحام مرغ CSO-ANN در برآورد سطح آب زیرزمینی موردبررسی قرار گرفت. در گام بعد با بهرهگیری از مدل منتخب هیبریدی تغییرات سطح آب زیرزمینی منطقه برای دوره آماری 2042-2022 پیشبینی شد. نتایج حاصل از مدل گردش عمومی جو نشان داد با افزایش دما میزان بارش کاهش می یابد و دمای شبیهسازی شده در کلیه مدلهای اقلیمی (SSp126، SSP245 و SSP585) مورد بررسی در دوره آتی (2022-2042) نسبت به دوره پایه در تمام ماهها افزایش داشته در حالیکه میانگین بارش روند مشخصی از خود نشان نداده است. همچنین نتایج حاصل از مدلسازی نشان داد مدل شبکه عصبی مصنوعی-موجک عملکرد بهتری نسبت به سایر مدلها در براورد سطح اب زیرزمینی دشت کوهدشت دارد همچنین نتایج مطابق مدل منتخب نشان داد این دشت طی سالهای 2042-2022 میزان 5/4- 3 متر افت سطح آب زیرزمینی دارد.
کلیدواژهها
موضوعات
عنوان مقاله [English]
The Impact of Climate Change Parameters on Groundwater Level Decline (Case Study: Kuhdasht-Lorestan)
نویسندگان [English]
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Ebrahim nohani
1
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hamidreza babaali
2
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reza dehghani
3
1
Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dez.C., Islamic Azad University, Dezful, Iran.
2
Associate Professor, Department of Civil Engineering, Islamic Azad University, Khorramabad branch, Khorramabad, Iran
3
PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Areeo, Khorramabad, Iran
چکیده [English]
In recent years, global warming, climatic fluctuations, and the pressure of water resource extraction have led to a decline in groundwater levels. Therefore, to prevent the intensification of the above trend and to optimally manage the exploitation of groundwater resources in plains, simulating and predicting groundwater levels is essential and inevitable. To predict groundwater level changes in the Kuhdasht aquifer, meteorological parameters were first predicted and analyzed using the CIMP6 General Circulation Model under various scenarios. Then, using temperature, precipitation, and aquifer extraction data from the 2002-2022 statistical period, the performance of hybrid Artificial Neural Network-Wavelet (WANN) and Artificial Neural Network-Creative Shooter (CSO-ANN) models in estimating groundwater levels was investigated. In the next step, using the selected hybrid model, groundwater level changes in the region were predicted for the 2022-2042 statistical period. The results from the General Circulation Model indicated that with increasing temperature, precipitation decreases. The simulated temperature in all investigated climate models (SSp126, SSP245, and SSP585) for the future period (2022-2042) showed an increase compared to the baseline period in all months, while the average precipitation did not show a clear trend. Furthermore, the modeling results showed that the Artificial Neural Network-Wavelet model performed better than other models in estimating the groundwater level of the Kuhdasht plain. The results from the selected model also indicated a groundwater level decline of 3 to 4.5 meters in this plain during the years 2022-2042.
کلیدواژهها [English]
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Groundwater
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Prediction
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Artificial Neural Network
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General Circulation Model
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