| تعداد نشریات | 285 |
| تعداد نشریات سازمان | 83 |
| تعداد شمارهها | 3,107 |
| تعداد مقالات | 34,629 |
| تعداد مشاهده مقاله | 49,712,016 |
| تعداد دریافت فایل اصل مقاله | 80,113,096 |
شبکه بهینه پایش آب زیرزمینی با استفاده از رویکرد ترکیبی دادهکاوی و الگوریتم ژنتیک NSGA-II در آبخوان شمیل- آشکارا، استان هرمزگان | ||
| پژوهش های آبخیزداری | ||
| مقاله 3، دوره 38، شماره 4 - شماره پیاپی 149، دی 1404، صفحه 23-52 اصل مقاله (1.79 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2025.368522.1614 | ||
| نویسندگان | ||
| حمید غلامی* 1؛ مجتبی محمدی2؛ مریم زارع3؛ حوریه زحمتکش کارمی4؛ آرش جمشیدی5 | ||
| 1استاد گروه منابعطبیعی دانشکده کشاورزی و منابعطبیعی، دانشگاه هرمزگان، بندرعباس، ایران | ||
| 2استادیار گروه مدیریت و کنترل بیابان، دانشکده علوم محیطی، برنامهریزی و توسعه پایدار، دانشگاه سراوان، سراوان، ایران | ||
| 3کارشناس ارشد گروه تحقیقات کاربردی شرکت سهامی آب منطقه ای استان هرمزگان، بندرعباس، ایران | ||
| 4کارشناس ارشد گروه پژوهش منابع آب زیری شرکت سهامی آب منطقه ای استان هرمزگان، بندرعباس، ایران | ||
| 5کارشناس ارشد گروه حفاظت و بهره برداری از منابع آب میناب، میناب، ایران | ||
| چکیده | ||
| مقدمه و هدف منابع آب زیرزمینی بهعنوان بخش بزرگی از منابع آب شیرین جهان، نقش حیاتی در تأمین نیازهای بشر و حفظ بومنظامها دارند. از سوی دیگر، بهرهبرداری بیرویه، تغییرات اقلیمی و آلودگی، این منابع ارزشمند را با چالشهای جدی مواجه ساخته است. کاهش سطح آب، کاهش کیفیت و فرونشست زمین از جمله پیامدهای منفی برداشت بیش از حد آب زیرزمینی است. مدیریت مؤثر و پایدار این منابع، مستلزم پایش دقیق و ارزیابی مستمر وضعیت کمی و کیفی آنهاست. شبکههای پایش آبهای زیرزمینی، با جمعآوری دادههای مربوط به سطح و کیفیت آب، امکان ارزیابی وضعیت منابع و شناسایی مشکلات را فراهم میکند. از سوی دیگر، طراحی بهینه این شبکهها، با توجه به محدودیتهای هزینهای و عملیاتی، چالشبرانگیز است. هدف این پژوهش، بررسی و بهینهسازی شبکه چاههای مشاهدهای موجود در دشت شمیل-آشکارا در شمالشرقی استان هرمزگان با استفاده از روشهای دادهکاوی و هوش مصنوعی، شامل تحلیل سلسلهمراتبی (AHP)، تحلیل مؤلفههای اصلی (PCA) و الگوریتم ژنتیک مرتبسازی نامغلوب نوع دوم (NSGA-II) بود. در این پژوهش، افزون بر بررسی بهبود کارایی شبکه پایش و کاهش هزینههای مرتبط، مکانهای بهینه برای ساخت چاههای مشاهدهای جدید نیز تعیین شد. مواد و روشها منطقه مطالعهشده، دشت شمیل-آشکارا در شمالشرقی استان هرمزگان، با مساحت 321/93 کیلومترمربع، همراه با سازندهای زمینشناسی متنوعی (شامل بنگستان، آغاجاری، گچساران، آسماری، رازک، هرمز، گورپی، میشان و آبرفتهای کواترنر) بود. آبخوان مطالعهشده در سازندهای کواترنر بود و کاربری زمین آن شامل مراتع فقیر، کشاورزی و مسکونی بود. در این پژوهش، ابتدا با استفاده از روش AHP، مکانهای مناسب برای ساخت چاههای مشاهدهای تعیین شد. هشت معیار شامل میانگین سطح آب زیرزمینی بلندمدت، نرخ کاهش سالانه، شیب تغییرات کاهش، تراکم چاههای بهرهبرداری، فاصله از رود، سازند زمینشناسی، کاربری زمین و فاصله از گسل درنظرگرفته شد. وزن هر معیار با استفاده از مقایسههای زوجی و نرمافزار Expert Choice محاسبه شد. سپس، با استفاده از نرمافزار ArcGIS، نقشه پایانی اولویتبندی مکانهای مناسب تهیه شد. در گام بعد، شبکه پایش کنونی با نتایج مکانیابی مقایسه شد. با استفاده از شبکههای تیسن و محاسبه میانگین امتیازات مکانیابی در هر چندضلعی، چاههای کماهمیت شناسایی شدند. برای اطمینان از صحت انتخاب، آزمونهای همگنی دادهها روی چاههای کماهمیت انجام شد. سپس، با استفاده از روش PCA، اهمیت نسبی چاههای پایش، تعیین شد. با حذف متوالی هر چاه و محاسبه ضریب همبستگی میان دادههای چاههای باقیمانده و اولین مؤلفه اصلی (PC1)، اهمیت نسبی هر چاه محاسبه شد. همچنین، نبودن قطعیت ناشی از حذف چاههای کماهمیت با استفاده از میانگین ضریب تغییرات سطح آب زیرزمینی ارزیابی شد. برای بررسی اثر حذف چاههای کماهمیت بر خطای درونیابی، از روش کریجینگ استفاده شد. دو سناریو شامل استفاده از تمام چاهها و حذف چاههای کماهمیت بررسی شد و خطای استاندارد و RMSE محاسبه شد. در پایان، با استفاده از الگوریتم NSGA-II، شبکه بهینه چاههای مشاهدهای تعیین شد. دو تابع هدف شامل تعداد چاههای مشاهدهای و RMSE سطح آب زیرزمینی کمینه شدند. روش IDW برای برآورد سطح آب زیرزمینی در چاههای حذفشده استفاده شد. نتایج و بحث نتایج AHP نشان داد که بیشترین اولویت ساخت چاه مشاهدهای بهدلیل تمرکز فعالیتهای کشاورزی، تراکم زیاد چاهها و کاهش شدید سطح آب مربوط به مناطق جنوبی و جنوبشرقی بود. در این تحلیل، بیشترین اهمیت مربوط به معیار تراکم چاههای بهرهبرداری با وزن 0/327 بود و پس از آن معیار کاهش سطح آب زیرزمینی (0/245) و کاربری زمین (0/15) بودند. نرخ ناسازگاری 0/04 محاسبه شد که نشاندهنده سازگاری مناسب قضاوتهای خبرگان بود. مقایسه شبکه پایش کنونی با نتایج AHP نشان داد که پنج چاه (W7، W9، W12، W14 و W16) با میانگین امتیاز مکانیابی کمتر از 0/113 از دیدگاه موقعیت مکانی نامناسب بودند. آزمونهای همگنی دادهها با استفاده از روش Pettitt و p-value کمتر از 0/05 نیز بیانگر نبودن همگنی دادههای این چاهها بود. نتایج PCA نشان داد که چاههای کماهمیت شناساییشده با این روش (W8، W10، W18، W21 با ضریبهای همبستگی کمتر از 0/20)، با چاههای شناساییشده با روش AHP متفاوت بودند که این تفاوت بهدلیل تفاوت ساختار و دادههای ورودی دو روش بود. زیرا، در روش PCA صرفاً از دادههای سطح آب استفاده میشود، اما در روش AHP معیارهای بیشتری درنظرگرفته میشود. بررسی نتایج کریجینگ نشان داد که حذف چاههای کماهمیت بر اساس AHP منجر به کاهش خطای استاندارد درونیابی از میانگین 4/8 به 4/2 متر شد. همچنین، حذف چاهها بر اساس PCA باعث افزایش این خطا از 4/8 به 3/5 متر شد. از سوی دیگر، در هر دو روش، حذف چاههای کماهمیت باعث افزایش RMSE شد. در روش AHP ریشه میانگین مربعات خطا، از 14/22به 17/08 متر (20/1 % افزایش) و در روش PCA از 14/22 به 14/41 متر (1/3% افزایش) یافت. الگوریتم NSGA-II برای شبکه پایش، پیکربندی بهینهای 16 چاهی را پیشنهاد داد که در مقایسه با شبکه اولیه 28 چاهی، 43% در تعداد چاهها کاهش مشاهده شد. افزون بر این، RMSE فقط 3/7% (از 14/22 به 15/26 متر) افزایش یافت. ضریب تغییرات سطح آب زیرزمینی در شبکه بهینه 0/89 محاسبه شد که نزدیک به اندازه آن در شبکه اصلی (0/92) بود. این یافته نشاندهنده حفظ اطلاعات مهم آبزمینشناختی در شبکه کاهشیافته بود. نتیجهگیری و پیشنهادها روش AHP برای تعیین مکانهای بهینه چاههای مشاهدهای کارآمد بود و استفاده از آن در سایر دشتها نیز توصیه میشود. همچنین، پیشنهاد میشود برای بهبود کارایی این روش، از متغیرهای دیگر و خروجی مدلهای آب زیرزمینی استفاده شود. افزون بر این، استفاده از روشهای PCA و NSGA-II سبب بهینهسازی شبکههای پایش خواهد شد. | ||
| کلیدواژهها | ||
| آب زیرزمینی؛ شبکه پایش؛ AHP؛ دشت شمیل-آشکارا؛ هرمزگان | ||
| عنوان مقاله [English] | ||
| Optimization Groundwater Monitoring Network Design Using a Combined Data Mining and NSGA-II Genetic Algorithm Approach in the Shamil-Ashkara Aquifer, Hormozgan Province | ||
| نویسندگان [English] | ||
| Hamid Gholami1؛ Mojtaba Mohammadi2؛ Maryam Zare3؛ Hourieh Zhatkesh Karmi4؛ Arash Jamshidi5 | ||
| 1Professor, Department of Natural Resources Engineering, Faculty of Agricultural and Natural Resources Engineering, University of Hormozgan, Bandar Abbas | ||
| 2Assistant Professor, Department of Desert Management and Control, Faculty of Environmental Sciences, Planning and Sustainable Development, University of Saravan, Saravan, Iran | ||
| 3M.Sc., Department of Applied Research, Hormozgan Regional Water Company, Bandar Abbas, Iran | ||
| 4M.Sc., Department of Groundwater Resources Research, Hormozgan Regional Water Company, Bandar Abbas, Iran | ||
| 5M.Sc., Department of Protection and Operation of Water Resources of Minab, Minab, Iran | ||
| چکیده [English] | ||
| Introduction and Goal Groundwater resources as a major portion of the world's freshwater reserves, play a vital role in meeting human needs and maintaining ecosystems. On the other hand, unsustainable extraction, climate change, and pollution have posed serious challenges to these valuable resources. Declining water levels, deteriorating quality, and land subsidence are among the negative consequences of excessive groundwater extraction. Effective and sustainable management of these resources requires precise monitoring and continuous assessment of their quantitative and qualitative status. Groundwater monitoring networks, by collecting data on water levels and quality, enable the evaluation of resource conditions and the identification of potential issues. On the other hand, the optimal design of these networks is challenging due to cost and operational constraints. The objective of this research was to investigate and optimize the existing network of observation wells in the Shamil-Ashkara aquifer, located in the northeast of Hormozgan province, using data mining and artificial intelligence methods, including Analytic Hierarchy Process (AHP), Principal Component Analysis (PCA), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In addition to examining improvements in monitoring network efficiency and reducing associated costs, this study also identified optimal locations for constructing new observation wells. Materials and Methods The study area, the Shamil-Ashkara plain in the northeast of Hormozgan province, covers an area of 321.93 km2 and features diverse geological formations (including the Bangestan, The Aghajari, Gachsaran, Asmari, Razak, Hormuz, Gurpi, Mishan, and Quaternary alluvium). Studied aquifer was located within the Quaternary formations, and the land use comprised poor rangeland, agriculture, and residential areas. In this research, suitable locations for constructing observation wells were initially determined using the Analytic Hierarchy Process (AHP) method. Eight criteria were considered, including the long-term average groundwater level, annual groundwater decline rate, slope of decline changes, density of exploitation wells, distance from the river, geological formation, land use, and distance from faults. The weight of each criterion was calculated using pairwise comparisons and Expert Choice software. Then, using ArcGIS software, final map prioritising suitable locations was prepared. In the next step, the current monitoring network was compared with the site selection results. Using Thiessen networks and calculating the average site selection scores in each polygon, less significant wells were identified. Homogeneity tests were performed on the less important wells to ensure the selection was correct. Then, using the PCA method, the relative importance of the monitoring wells was determined. By sequentially removing each well and calculating the correlation coefficient between the data of the remaining wells and the first principal component (PC1), the relative importance of each well was calculated. Additionally, the lack of certainty resulting from the removal of insignificant wells was assessed using the average coefficient of variation of groundwater levels. To investigate the effect of removing insignificant wells on interpolation error, the kriging method was used. Two scenarios were examined, including the use of all wells and the removal of insignificant wells, and the standard error and RMSE were calculated. At the end, using the NSGA-II algorithm, the optimal network of observation wells was determined. Two objective functions, including the number of observation wells and the minimum RMSE of groundwater level, were minimised. The IDW method was used to estimate the groundwater level in the deleted wells. Results and Discussion The results of AHP show that the highest priority for constructing observation wells was due to the concentration of agricultural activities, the high density of wells, and the severe decline in water levels in the southern and southeastern. In this analysis, the most important factor was the density criterion of exploitation wells with a weight of 0.327, followed by the criterion of groundwater level reduction (0.245) and land use (0.15). The inconsistency rate was calculated as 0.04, indicating an appropriate consistency of the expert judgments. Comparing the current monitoring network with the AHP results showed that five wells (W7, W9, W12, W14, and W16) had an average location score of less than 0.113, indicating they were unsuitable from a location perspective. The homogeneity tests of the data using the Pettitt method and a p-value of less than 0.05 also indicated the lack of homogeneity of the data from these wells. The results of PCA showed that the insignificant wells identified by this method (W8, W10, W18, W21 with correlation coefficients of less than 0.20) were different from the wells identified by the AHP method, and this difference was due to the differing structure and input data of the two methods. This is because in the PCA method, only water level data is used, whereas in the AHP method more criteria are considered. The analysis of the kriging results showed that the removal of insignificant wells based on AHP led to a decrease in the interpolation standard error from an average of 4.8 to 4.2 m. Additionally, the removal of wells based on PCA caused an increase in this error from 4.8 to 3.5 m. On the other hand, in both methods, the removal of less important wells led to an increase in RMSE. In the AHP method, the root mean square error decreased from 22.14 to 17.08 m (a 1.20% increase), while in the PCA method it changed from 22.14 to 41.14 m (a 3.1% increase). The NSGA-II algorithm for the monitoring network proposed an optimal configuration of 16 wells, which represented a 43% reduction in the number of wells compared to the initial network of 28 wells. Moreover, the RMSE only increased by 7.3% (from 22.14 to 26.15 m). The coefficient of variation of groundwater level in the optimal network was calculated to be 0.89, which was close to its value in the main network (0.92). This finding indicates the preservation of important hydrogeological information in the reduced network. Conclusion and Suggestions The AHP method for determining optimal locations for observation wells was effective and its use is also recommended in other plains. Furthermore, it is suggested to employ other variables and the output of groundwater models to enhance the efficiency of this method. Additionally, the use of PCA and NSGA-II methods will lead to the optimisation of monitoring networks. | ||
| کلیدواژهها [English] | ||
| AHP, Dasht Shamil-Ashkara, groundwater, Hormozgan, monitoring network | ||
| مراجع | ||
|
Al Saati MN. 1980. Contribution to the study of the transitional current for short times (Doctoral dissertation, Toulouse), France. Albert T, Foster S, Kemper K, Garduno H, Nanni M. 2004. Groundwater monitoring requirements for managing aquifer response and quality threats..In W.W.W-G. Yeh, N.E. Kaden, & A.I. El-Fadel (Eds.), Bringing Groundwater Quality Research to the Watershed Scale. IAHS Press. pp. 3-10. Alfonso L, Lobbrecht A, Price R. 2010. Information theory-based approach for location of monitoring water level gauges in polders. Water Resources Research. 46(3): W03528. https://doi.org/10.1029/2009WR008251. Amiri V. 2021. Optimization of groundwater quality monitoring network using geostatistical method. Journal of Arid Biome: 10(2): 37-52. Banadkook FB, Ehteram M, Ahmed AN, Teo F Y, Fai CM, Afan HA, Sapitang M, El-Shafie A. 2020. Enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm. Natural Resources Research. 29: 3233-3252. Bilal M, Oyedele LO, Akinade OO, Ajayi SO, Alaka HA, Owolabi HA. 2018. Artificial neural network approach for predicting water quality index: a case study in Malaysia. Journal of Cleaner Production. 189: 376-386. Canton H. 2021. Food and agriculture organization of the United Nations—FAO. In The Europa directory of international organizations 2021. Routledge. pp. 297-305. Castro LM, Gironás J, Fernández B. 2019. Spatial estimation of daily precipitation in regions with complex relief and scarce data using terrain orientation. Journal of Hydrology. 576: 788-806. Chen, YC, Wei C, Yeh HC. 2017. Rainfall network design using entropy and kriging approach. Journal of Hydrology. 549: 350-364. Chen Z, Liu Z, Yin L, Zheng W. 2022. Statistical analysis of regional air temperature characteristics before and after dam construction. Urban Climate. 41: 101085. https://doi.org/https://doi.org/10.1016/j.uclim.2022.101085 Chowdhury A, Jha MK, Chowdary VM. 2010. Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur district, West Bengal, using RS, GIS and MCDM techniques. Environmental Earth Sciences. 59(6): 1209-1222. Cressie NAC. 1993. Statistics for Spatial Data. New York, NY: John Wiley & Sons. Dokou Z, Pinder GF. 2009. Optimal search strategy for the definition of a DNAPL source. Journal of Hydrology. 376(3-4): 542-556. Ebrahimi, H, Rajaee T. 2017. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change. 148: 181-191. El Bilali A, Taleb A, Brouziyne Y. 2021. Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agricultural Water Management. 245: 106625. Esquivel JM, Morales GP, Esteller MV. 2015. Groundwater monitoring network design using GIS and multicriteria analysis. Water Resources Management. 29: 3175-3194. FAO. 2021. Aquastat database. Aquastat. website. https://www.fao.org/aquastat/statistics/query/index.html. Accessed February 2021 Farzin S, Ifaei P, Farzin N, Hassanzadeh Y, Aalami MT. 2018. An investigation on changes and prediction of Urmia Lake water surface evaporation by chaos theory. International Journal of Environmental Science and Technology. 15(6): 1169-1184. Forman EH, Gass SI. 2001. The analytic hierarchy process—an exposition. Operations Research. 49(4): 469-486. Gautam K, Sharma P, Dwivedi S, Singh A, Gaur VK, Varjani S, Srivastava JK, Pandey A, Chang JS, Ngo HH. 2023. A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil. Environmental Research. 225: 115592. Ghahroudi Tali M, Derafshi K. 2015. The study of chaos in the flood risk pattern of Tehran. Journal of Spatial Analysis Environmental Hazarts. 2(2): 1-16. Ghorbani MA, Deo RC, Karimi V, Yaseen Z. M, Terzi O. 2018. Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey. Stochastic Environmental Research and Risk Assessment. 32: 1683-1697. Gong W, Yang D, Gupta HV, Nearing G. 2016. Estimating information entropy for hydrological data: One-dimensional case. Water Resources Research. 52(7): 5418-5432. Heath RC. 1976. Design of Ground–Water Level Observation–Well Programs. Ground Water. 14: 71-77. Huang S, Lyu Y, Sha H, Xiu L. 2021. Seismic performance assessment of unsaturated soil slope in different groundwater levels. Landslides. 18(8): 2813-2833. Hudak PF. 2006. Heuristic for constructing minimum-well groundwater monitoring configurations at waste storage facilities. Journal of Environmental Science and Health, Part A. 41(2): 185-193. Hussain I, Pilz J, Spoeck G. 2021. Entropy-based sampling design for spatial data. Environmental Monitoring and Assessment. 193(1): 1-17. Isaaks EH. Srivastava RM. 1989. An Introduction to Applied Geostatistics. Oxford University Press. Ishizaka A, Labib A. 2011. Review of the main developments in the analytic hierarchy process. Expert Systems with Applications. 38(11): 14336-14345. Jee DH, Kang KJ. 2000. A method for optimal material selection aided with decision making theory. Materials and Design. 21(3):199-206. https://doi.org/https://doi.org/10.1016/S0261-3069(99)00066-7 Jha MK, Chowdhury A, Chowdary, VM, Peiffer S. 2016. Groundwater management and development by integrated remote sensing and geographic information systems: Prospects and constraints. Water Resources Management, 21(2): 427-467. Jolliffe IT. 2002. Principal Component Analysis. Springer Series in Statistics, 2nd ed. Springer, New York. Karakaya N, Evrendilek F, Gungor K, Onal D. 2020. Predicting water quality parameters using artificial neural networks: A case study for Egirdir Lake, Turkey. Journal of Environmental Science and Health, Part A. 55(4): 374-384. Karmakar S, Gupta K, Gupta A. 2018. Groundwater quality monitoring network design using GIS-based multi-criteria analysis: A case study. Environmental Earth Sciences. 77(19): 1-18. Keum J, Coulibaly P. 2017. Sensitivity of entropy method to time series length in hydrometric network design. Journal of Hydrologic Engineering. 22(9): 04017028. Komasi M, Goudarzi H. 2021. Multi-objective optimization of groundwater monitoring network using a probability Pareto genetic algorithm and entropy method (Case study: Silakhor plain). Journal of Hydroinformatics, 23(1): 136-150. Kontos YN, Kassandros T, Perifanos K, Karampasis M, Katsifarakis KL, Karatzas K. 2022. Machine learning for groundwater pollution source identification and monitoring network optimization. Neural Computing and Applications. 34(22): 19515-19545. Kumar H, Syed TH, Amelung F, Agrawal R, Venkatesh A. 2022. Space-time evolution of land subsidence in the National Capital Region of India using ALOS-1 and Sentinel-1 SAR data: Evidence for groundwater overexploitation. Journal of Hydrology. 605: 127329. Kumar T, Gautam AK, Kumar T. 2020. Appraising the accuracy of GIS-based Multi-criteria decision making technique for delineation of Groundwater potential zones. Water Resources Management. 34(1): 169-191. Lee C, Paik K, Yoo DG, Kim JH. 2012. Efficient method for optimal placing of water quality monitoring stations for an ungauged basin. Journal of Environmental Management. 101: 42-49. Li J, Lu C, Hu J, Chen Y, Ma J, Chen J,Shu L. 2025. Determining the Groundwater Level in Hilly and Plain Areas From Multisource Observation Data Combined With a Machine Learning Approach. Hydrological Processes. 39(2): e70088. Liu S, Liu Y, Wang C, Dang X. 2022. The distribution characteristics and human health risks of high-fluorine groundwater in coastal plain: A case study in Southern Laizhou Bay, China. Frontiers in Environmental Science. 10: 901637. Machiwal D, Jha MK, Mal BC. 2011. Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resources Management. 25(5): 1359-1386. Machiwal D, Rangi N, Sharma A. 2015. Integrated knowledge and data-driven approaches for groundwater potential zoning using GIS and multi-criteria decision making techniques on hard-rock terrain of Ahar catchment, Rajasthan, India. Environmental Earth Sciences. 73(4): 1871-1888. Malczewski J. 2006. GIS‐based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science. 20(7): 703-726. Malik A, Kumar A, Gaffar A. 2019. Designing a network of groundwater quality monitoring stations using GIS and entropy. Environmental Processes. 6(1): 163-184. McKinney DC, Loucks DP. 1992. Network design for predicting groundwater contamination. Water Resources Research. 28(1): 133-147. Mirzaei-Nodoushan F, Bozorg Haddad O, Khayat Kholghi M. 2019. Optimization and development of groundwater-level monitoring network in Eshtehard plain. Watershed Engineering and Management. 11(1): 273-282. Mirzaie-Nodoushan F, Bozorg-Haddad O, Loáiciga HA. 2017. Optimal design of groundwater-level monitoring networks. Journal of Hydroinformatics. 19(6): 920-929. Mishra AK, Coulibaly P. 2009. Developments in hydrometric network design: A review. Reviews of Geophysics. 47(2): RG2001. Mu E, Pereyra-Rojas M. 2017. Understanding the Analytic Hierarchy Process. In Practical Decision Making: An Introduction to the Analytic Hierarchy Process (AHP). Springer, Cham. pp. 13-22. Noorbeh P, Roozbahani A, Kardan Moghaddam H. 2020. Annual and monthly dam inflow prediction using Bayesian networks. Water Resources Management. 34: 2933-2951. Nourani V, Baghanam AH, Adamowski J, Kisi O. 2014. Applications of hybrid wavelet–artificial intelligence models in hydrology: A review. Journal of Hydrology. 514: 358-377. Ouyang Y, Nkedi-Kizza P, Wu QT, Shinde D, Huang CH. 2010. Assessment of seasonal variations in surface water quality. Water Research. 40(20): 3800-3810. Park SY, Choi JH, Wang S, Park SS. 2014. Design of a water quality monitoring network in a large river system using the genetic algorithm. Ecological Modelling. 199(3): 289-297. Peters HJ. 1972. Criteria for groundwater level data networks for hydrologic and modeling purposes. Water Resources Research. 8(1): 194-200. https://doi.org/https://doi.org/10.1029/WR008i001p00194 Rahnama MR. 2021. Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustainable Cities and Society. 64: 102548. Ran Y, Li X, Ge Y, Lu X, Lian Y. 2015. Optimal selection of groundwater-level monitoring sites in the Zhangye Basin, Northwest China. Journal of Hydrology. 525: 209-215. https://doi.org/https://doi.org/10.1016/j.jhydrol.2015.03.059 Saaty TL, Vargas LG. 2012. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process (International Series in Operations Research and Management Science, Vol. 175). Springer. Sama A, Hamid A, Parviz A, Sirus J. 2020. Optimal design of groundwater monitoring networks using gamma test theory. Hydrogeology Journal. 28(4): 1389-1402. Sánchez-Martos F, Jimenez-Espinosa R, Pulido-Bosch A. 2001. Mapping groundwater quality variables using PCA and geostatistics: A case study of Bajo Andarax, southeastern Spain. Hydrological Sciences Journal. 46(2): 227-242. Sattari MT, Mirabbasi R, Sushab RS, Abraham J. 2018. Prediction of groundwater level in Ardebil plain using support vector regression and M5 tree model. Groundwater. 56(4): 636-646. Shelar A, Singh AV, Dietrich P, Maharjan RS, Thissen A, Didwal PN, Shinde M, Laux P, Luch A, Mathe V. 2022. Emerging cold plasma treatment and machine learning prospects for seed priming: a step towards sustainable food production. RSC advances. 12(17): 10467-10488. Singh CK, Katpatal YB. 2020. A Review of the historical background, needs, design approaches and future challenges in groundwater level monitoring networks. Journal of Engineering Science and Technology Review. 13(2):191-201. https://doi.org/10.25103/jestr.132.22 Singh KP, Malik A, Sinha S. 2013. Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques (Case study). Analytica Chimica Acta. 538(1-2): 355-374. Singh LK, Jha MK, Chowdary VM. 2019. Assessing the accuracy of GIS-based Multi-Criteria Decision Analysis approaches for mapping groundwater potential. Ecological Indicators. 103: 194-209. Singha K, Navarre‐Sitchler A. 2022. The importance of groundwater in critical zone science. Groundwater. 60(1): 27-34. Strobl RO, Robillard PD, Shannon RD, Day RL, McDonnell AJ. 2016. A water quality monitoring network design methodology for the selection of critical sampling points: Part I. Environmental Monitoring and Assessment. 112(1): 137-158. Tara F, Bazrafshan O. 2023. Spatial and temporal analysis of groundwater quality of Shamil-Ashkara aquifer. Journal of Rainwater Catchment Systems. 11 (3) : 59-72. (In Persian). https://doi.org/10.22092/jrwcs.2023.363999.1235 Wang S, Zhang K, Chao L, Li D, Tian X, Bao H, Chen G, Xia Y. 2021. Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards. Journal of Hydrology. 603: 126964. Wang WC, Chau KW, Xu DM, Chen XY. 2015. Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resources Management. 29(8): 2655-2675. Yang FG, Cao SY, Liu, Xn, Yang KJ. 2008. Design of groundwater level monitoring network with ordinary kriging. Journal of Hydrodynamics. 20(3): 339-346. Yang YS, Kalin RM, Zhang Y, Lin X, Zou,L. 2018. Multi-objective optimization for sustainable groundwater resource management in a semiarid catchment. Hydrological Sciences Journal. 46(1): 55-72. Yangxiao Z, Te Stroet CBM, Van Geer FC. 1991. Using kalman filtering to improve and quantify the uncertainty of numerical groundwater simulations: 2. Application to Monitoring Network Design. Water Resources Research. 27(8): 1995-2006. https://doi.org/https://doi.org/10.1029/91WR00510 Yeh HC, Chen YC, Chang CH, Ho CH, Wei C. 2008. Rainfall network design using kriging and entropy. Hydrological Processes. 22(3): 340-346. Zare M, Koch M. 2018. Groundwater level fluctuations simulation and prediction by ANFIS-and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: Application to the Miandarband plain. Journal of Hydro-Environment Research. 18: 63-76. Zarkesh MK. 2005. Decision support system for floodwater spreading site selection in Iran. (PhD thesis, 222 pages). Wageningen University, Wageningen, the Netherlands. Zhang Y, Guo F, Meng W, Wang XQ. 2016. Water quality assessment and source identification of Daliao river basin using multivariate statistical methods. Environmental Monitoring and Assessment. 188(6): 1-12. Zhou, Y, Li W. 2016. A review of regional groundwater flow modeling. Geoscience Frontiers. 2(2): 205-214. | ||
|
آمار تعداد مشاهده مقاله: 583 تعداد دریافت فایل اصل مقاله: 3 |
||