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پیش‌بینی تاب‌آوری آبخیز دشت مرودشت با استفاده از یک چارچوب ترکیبی مبتنی بر WoE | ||
| پژوهش های آبخیزداری | ||
| مقاله 8، دوره 39، شماره 2 - شماره پیاپی 151، تیر 1405، صفحه 116-137 اصل مقاله (1.22 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2025.371489.1647 | ||
| نویسندگان | ||
| سعید علیزاده1؛ رضا قضاوی* 2؛ ابراهیم امیدوار3 | ||
| 1دانشجوی دکتری علوم و مهندسی آبخیزداری، دانشگاه کاشان | ||
| 2استاد دانشکده منابع‌طبیعی و علوم زمین دانشگاه کاشان | ||
| 3دانشیار دانشکده منابع طبیعی و علوم زمین دانشگاه کاشان | ||
| چکیده | ||
| مقدمه و هدف در قرن پیش‌رو با تشدید خشک‌سالی‌ها و افزایش فعالیت‌های انسانی بر منابع آب‌های زیرزمینی، ارزیابی تاب‌آوری سامانههای آبخوان به‌عنوان یک شاخص مهم پایداری منابع آب، اهمیت ویژه‌ای دارد. ازاین‌رو، برای سنجش توانایی آبخوان در بازگشت به وضعیت مطلوب پس از فشارهای آب‌شناختی، از شاخص کمّی (CRS) به‌عنوان معیار، استفاده می‌شود. دشت مرودشت با 148 هزار هکتار زمین‌های زارعی آبی، 22 هزار هکتار زمین‌های دیم، قطب کشاورزی استان فارس است. اما به‌دلیل برداشت بی‌رویه از منابع آب زیرزمینی چالش‌های مختلفی مانند افت سطح آب زیرزمینی، فرونشست و شکاف‌های پرشماری رخداده است که خطر فرونشست در نواحی باستانی تخت‌جمشید و نقش‌رستم نیز نمایان است. ازاین‌رو، این پژوهش با رویکرد جامع تلفیق مدل وزن‌دهی شواهد (WoE)، روش‌های انتخاب ویژگی پیشرفته و روش‌های اعتبارسنجی و با هدف پیش‌بینی تاب‌آوری آب‌های زیرزمینی در آبخیز دشت مرودشت اجرا شد و نقشه کاربردی و قابل اطمینان توزیع مکانی تاب‌آوری آب‌های زیرزمینی این دشت، برای تصمیم‌گیری‌‌‌های مدیریتی تولید شد. مواد و روش‌ها در این پژوهش، ابتدا ۲۱ متغیر محیطی، آب‌زمین‌شناختی و اقلیمی به‌عنوان عامل‌های تعیین‌کنندة وضعیت آب‌های زیرزمینی شناسایی و به‌‌شکل لایه‌های شبکه‌ای تهیه شدند. سپس، با به‌کارگیری یک رویکرد چندمرحله‌ای ویژگی‌ها مبتنی بر پراکنش، همبستگی، هم‌خطی و اطلاعات متقابل، انتخاب شدند. در این پژوهش، 10 عامل از مؤثرترین عامل‌ها (تراکم کرنل چاههای بهره‌برداری، تراکم آبراهه، فاصله از چاههای کشاورزی، فاصله از گسل، انحنای سطح، فاصله از چاههای صنعتی، میانگین افت، ضخامت آبخوان، فاصله از آبراهه اصلی، تراکم چاههای بهره برداری) برای مدل‌سازی نهایی انتخاب شدند. روابط میان شاخص CRS و این عامل‌ها با روش ترکیب شواهد و محاسبه وزن‌های WoE تعیین شد. با جمع وزن‌هایWoE ، نقشه نهایی تاب‌آوری تولید و سپس بر اساس روش طبقه‌بندی چندکی به پنج طبقه تقسیم شد. اعتبارسنجی مدل با 3 روش اعتبارسنجی متقاطع طبقه‌بندی‌شده، روش بوت‌استرپ (1000 تکرار) و تحلیل واسنجی، انجام شد. نتایج و بحث یافته‌های اعتبارسنجی مدل نشان داد که عملکرد مدل در پیش‌بینی شاخص تاب‌آوری آب‌های زیرزمینی (CRS) بسیارخوب (920/0= AUC) و پایداری آماری آن قابل اطمینان است. در آبخیز مطالعه‌شده تاب‌آوری آب زیرزمینی با طبقات مختلف و دقت مناسب، شناسایی شد. نقشه نهایی بر اساس روش طبقه‌بندی چندکی به پنج طبقه بسیارکم (0/223 - 0)، کم (0/367 – 0/223)، متوسط (0/503 – 0/367)، زیاد (0/669 – 0/503)، خیلی‌زیاد (1 – 0/669)، تقسیم شد. این طبقات منعکس‌کننده تغییرپذیری ذاتی داده‌ها بودند. زیرا بر اساس توزیع تجربی اندازه‌های شاخص بهنجارشده، که از ترکیب وزن‌های WoE عامل‌های مؤثر محاسبه شدند، هر طبقه تقریباً شامل 20% از سطح مطالعه‌شده بود. در این پژوهش، اندازه‌های کم شاخص تاب‌آوری در بیشتر مناطق (میانگین = 0/048) بیانگر تاب‌آوری بسیارکم سامانة آبخوان در برابر فشارهای آب‌شناختی بود. نتیجه‌گیری و پیشنهادها بر اساس نتایج این پژوهش می‌توان مناطق بحرانی با کمترین تاب‌آوری را شناخت. از این‌رو، پیشنهاد می‌شود بر پایة نتایج به‌دست آمده مناطقی که در طبقه بسیار کم تاب‌آوری هستند باید در اولویت اول برنامه‌ریزی‌های مدیریتی مانند کاهش برداشت‌های غیرمجاز، اجرای طرح‌های تغذیه مصنوعی، ایجاد مناطق حفاظتی و نظارتی فنی- حقوقی باشند و در آنها نظارت شدید انجام شود. افزون بر این، پیشنهاد می‌شود از این چارچوب مدل‌سازی به‌عنوان یک مبنای علمی قابل تعمیم برای ارزیابی تاب‌آوری آب‌های زیرزمینی در دیگر دشت‌های بحرانی کشور استفاده شود. | ||
| کلیدواژه‌ها | ||
| آب زیرزمینی؛ بوت استرپ؛ شاخص تاب‌آوری؛ مدل‌سازی؛ مدیریت منابع آب | ||
| عنوان مقاله [English] | ||
| Forecasting the Rresilience of the Marvdasht Plain Watershed Using a Combined WoE-Based Framework | ||
| نویسندگان [English] | ||
| Saeed Alizadeh1؛ Reza Ghazavi2؛ Ebrahim Omidvar3 | ||
| 1Ph.D. Candidate in Watershed Sciences and Engineering, Department of Nature Engineering, University of Kashan | ||
| 2Professor in Watershed Sciences and Engineering, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan | ||
| 3Associate Professor in Watershed Sciences and Engineering, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan | ||
| چکیده [English] | ||
| Introduction and Goal In the coming century, with the intensification of droughts and increasing human activities on groundwater resources, assessing the resilience of aquifer system as an important indicator of water resource sustainability is of particular importance. Therefore, to measure the ability of an aquifer to return to its desired state after hydrological pressures, a quantitative index (CRS) was used as a criterion. The Marvdasht Plain, with 148,000 ha of irrigated farmland and 22,000 hectares of rainfed farmland, is the agricultural hub of Fars Province. However, due to excessive extraction of groundwater resources, various challenges have occurred, such as falling groundwater levels, subsidence, and numerous cracks, and the risk of subsidence is also evident in the ancient areas of Persepolis and Naqsh-e Rostam. Therefore, this study was conducted with a comprehensive approach combining the Weight of Evidence (WoE) model, advanced feature selection techniques, and validation methods, with the aim of predicting groundwater resilience in the Marvdasht plain watershed, and a practical and reliable map of the spatial distribution of groundwater resilience in this plain was produced for management decisions. Materials and Methods In this study, 21 environmental, hydrogeological, and climatic variables were initially identified as factors determining groundwater status and prepared in the form of raster layers. Then, using a multi-step approach, features based on dispersion, correlation, collinearity, and mutual information were selected. In this study, 10 of the most effective factors (including kernel density of exploitation wells, stream density, distance from agricultural wells, distance from fault, surface curvature, distance from industrial wells, average drop, aquifer thickness, distance from main stream, production well density) were selected for final modeling. The relationships between the CRS index and these factors were determined using the evidence synthesis method and calculating WoE weights. By summing the WoE weights, the final resilience map was generated and then classified into five classes based on the quantile classification method. Model validation was performed using three methods: random cross-validation, the bootstrap method (1,000 replicates), and calibration analysis. Results and Discussion The model validation findings showed that the model's performance in predicting the groundwater resilience index (CRS) was very good (AUC = 0.920) and its statistical stability was reliable. In the studied watershed, groundwater resilience was identified with different classes and appropriate accuracy. The final map, was classified into five classes based on the quantile classification method: very low (0 - 0.223), low (0.223 - 0.367), medium (0.367 - 0.503), high (0.503 - 0.669), and very high (0.669 - 1.000). These classes reflected the inherent variability of the data. Because based on the empirical distribution of normalized index sizes, which were calculated from the combination of WoE weights of the effective factors, each class comprised approximately 20% of the studied area. In this study, low resilience index values in most of the area (mean = 0.48) indicated very low resilience of the aquifer system to hydrological pressures. Consequently, zones identified with the lowest resilience should be prioritized for critical management interventions, such as reducing groundwater withdrawals, implementing artificial recharge projects, and establishing intensive monitoring networks. Conclusion and Suggestions Based on the results of this research, it is possible to identify the critical areas with the lowest resilience. Therefore, it is recommended that, based on the results obtained, areas that are in the very low resilience category should be given top priority in management planning, such as reducing unauthorized withdrawals, implementing artificial recharge schemes, establishing technical-legal protection and monitoring areas, and that strict monitoring be carried out in them. Also, it is suggested that this modeling framework be used as a generalizable scientific basis for assessing groundwater resilience in other critical plains in the country. | ||
| کلیدواژه‌ها [English] | ||
| Bootstrap, groundwater, modeling, resilience index, water resources management | ||
| مراجع | ||
|
AbdelRahman MA. 2023. An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications. Rendiconti Lincei. Scienze Fisiche e Naturali. 34(3): 767–808. https://doi.org/10.1007/s12210-023-01155-3 Alao JO, Bello AY, Lawal HA, Abdullahi D. 2024. Assessment of groundwater challenge and the sustainable management strategies. Results in Earth Sciences. 2: 100049. https://doi.org/10.1016/j.rines.2024.100049 Alizadeh A, Keshavarz A. 2005. Status of agricultural water use in Iran. In Water conservation, reuse, and recycling: Proceedings of an Iranian-American Workshop. Washington DC, USA: National Academies Press. Vol. 4, pp. 94–105. Al-Ruzouq R, Shanableh A, Merabtene T, Siddique M, Khalil MA, Idris A, Almulla E. 2019. Potential groundwater zone mapping based on geo-hydrological considerations and multi-criteria spatial analysis: North UAE. Catena. 173: 511–524. https://doi.org/10.1016/j.catena.2018.10.037 Andualem TG, Demeke GG. 2019. Groundwater potential assessment using GIS and remote sensing: A case study of Guna tana landscape, upper Blue Nile Basin, Ethiopia. Journal of Hydrology: Regional Studies. 24: 100610. https://doi.org/10.1016/j.ejrh.2019.100610 Behboudian M, Kerachian R, Pourmoghim P. 2021. Evaluating the long-term resilience of water resources systems: Application of a generalized grade-based combination approach. Science of the Total Environment. 786: 147447. https://doi.org/10.1016/j.scitotenv.2021.147447 Bhere S, Reddy MJ. 2022. Assessment of Reliability, Resilience, and Vulnerability (RRV) of terrestrial water storage using Gravity Recovery and Climate Experiment (GRACE) for Indian river basins. Remote Sensing Applications: Society and Environment. 28: 100851. https://doi.org/10.1016/j.rsase.2022.100851 Carruthers G, Tinning G. 2003. Where, and how, do monitoring and sustainability indicators fit into environmental management systems? Australian Journal of Experimental Agriculture, 43(3):307–323. https://doi.org/10.1071/EA00177 Deshmukh MM, Elbeltagi A, Kouadri S. 2022. Climate change impact on groundwater resources in semi-arid regions. In Climate Change Impact on Groundwater Resources: Human Health Risk Assessment in Arid and Semi-arid Regions. Cham: Springer International Publishing. pp. 9–23. https://doi.org/10.1007/978-3-031-04707-7_2 Dillon P, Fernández Escalante E, Megdal S. B, Massmann G. 2020. Managed aquifer recharge for water resilience. Water. 12(7): 1846. https://doi.org/10.3390/w12071846 Edrisnia S, Pahlevanroy A, Moghadamnia A, Nazarisamani A, Miri A. 2017. Investigation of the role of structural elements on the water resource abundance in the Maharloo Karst region using RS and GIS. Journal of Range and Watershed Management. 70(2): 263–275. https://doi.org/10.22059/jrwm.2017.202612.982 Feng S, Kang S, Huo Z, Chen S, Mao X. 2008. Neural networks to simulate regional ground water levels affected by human activities. Groundwater. 46(1): 80–90. https://doi.org/10.1111/j.1745-6584.2007.00366.x Fooladi M, Golmohammadi MH, Safavi HR, Singh VP. 2021. Application of meteorological drought for assessing watershed health using fuzzy-based reliability, resilience, and vulnerability. International Journal of Disaster Risk Reduction. 66: 102616. https://doi.org/10.1016/j.ijdrr.2021.102616 Fu G, Crosbie RS, Barron O, Charles SP, Dawes W, Shi X, Li C. 2019. Attributing variations of temporal and spatial groundwater recharge: A statistical analysis of climatic and non-climatic factors. Journal of Hydrology. 568: 816–834. https://doi.org/10.1016/j.jhydrol.2018.11.022 Gambolati G. 2005. Anthropogenic land subsidence. In Encyclopedia of Geophysics. https://doi.org/10.1002/0470848944.hsa164b Gillespie-Marthaler L, Nelson K, Baroud H, Abkowitz M. 2019. Selecting indicators for assessing community sustainable resilience. Risk Analysis. 39(11): 2479–2498. https://doi.org/10.1111/risa.13344 Gogu RC, Dassargues A. 2000. Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environmental Geology. 39(6): 549–559. https://doi.org/10.1007/s002540050466 Gorelick SM, Zheng C. 2015. Global change and the groundwater management challenge. Water Resources Research. 51(5): 3031–3051. https://doi.org/10.1002/2014WR016825 Granata F, Di Nunno F. 2025. Pathways for hydrological resilience: Strategies for adaptation in a changing climate. Earth Systems and Environment. pp. 1–29. https://doi.org/10.1007/s41748-024-00567-x Guo W, Yu L, Huang L, Wang B, Wang H. 2024. A framework for analyzing the most relevant indicators of ecohydrology in a changing environment. Journal of Water and Climate Change. 15(11): 5397–5417. https://doi.org/10.2166/wcc.2024.122 Haile MM. 2022. Approaches for delineating groundwater recharge potential zone using fuzzy logic model. Geofluids. 2022: 3637230. https://doi.org/10.1155/2022/3637230 Hoa NT, Vinh NQ. 2018. IOP Conference Series: Earth and Environmental Science, 143(1), 012066. https://doi.org/10.1088/1755-1315/143/1/012066 Hashimoto T, Stedinger JR, Loucks DP. 1982. Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resources Research. 18(1): 14-20. https://doi.org/10.1029/WR018i001p00014 Holling CS. 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics. 4: 1–23. Hund SV, Allen DM, Morillas L, Johnson MS. 2018. Groundwater recharge indicator as tool for decision makers to increase socio-hydrological resilience to seasonal drought. Journal of Hydrology. 563: 1119–1134. https://doi.org/10.1016/j.jhydrol.2018.05.069 Javadinejad S, Hannah D, Krause S, Naseri M, Dara R, Jafary F. 2020. Building socio-hydrological resilience “improving capacity for building a socio-hydrological system resilience”. Safety in Extreme Environments. 2(3): 205–218. https://doi.org/10.1007/s42797-020-00024-8 Jones L. 2019. Resilience isn't the same for all: Comparing subjective and objective approaches to resilience measurement. Wiley Interdisciplinary Reviews: Climate Change. 10(1): e552. https://doi.org/10.1002/wcc.552 Karni E. 2007. Foundations of Bayesian theory. Journal of Economic Theory. 132(1): 167–188. Khatri N, Tyagi S. 2015. Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas. Frontiers in Life Science. 8(1): 23–39. https://doi.org/10.1080/21553769.2014.933716 Khoshtinat S, Aminnejad B, Hassanzadeh Y, Ahmadi H. 2019. Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater. Journal of Hydroinformatics. 21(5): 745–760. https://doi.org/10.2166/hydro.2019.127 Kumar N, Sinha J, Madramootoo CA, Goyal MK. 2020. Quantifying groundwater sensitivity and resilience over peninsular India. Hydrological Processes. 34(26): 5327–5339. https://doi.org/10.1002/hyp.13945 La Vigna F. 2022. Urban groundwater issues and resource management, and their roles in the resilience of cities. Hydrogeology Journal. 30(6): 1657–1683. https://doi.org/10.1007/s10040-022-02517-1 Lin YC, Kuo ED, Chi WJ. 2021. Analysis of meteorological drought resilience and risk assessment of groundwater using signal analysis method. Water Resources Management. 35(1): 179–197. https://doi.org/10.1007/s11269-020-02718-x Li Y, Lence BJ. 2007. Estimating resilience for water resources systems. Water Resources Research. 43(7). https://doi.org/10.1029/2006WR005636 Majid SI, Kumar M, Kumar P, Verma NK. 2023. GIS-based flood susceptibility mapping of Srinagar district, India using weights-of-evidence (WofE), frequency ratio (FR) and fuzzy gamma operator (FGO). Journal of the Indian Society of Remote Sensing. 51(12): 2421–2446. https://doi.org/10.1007/s12524-023-01776-z Mao Z, Todd M. 2013. Statistical modeling of frequency response function estimation for uncertainty quantification. Mechanical Systems and Signal Processing. 38(2): 333–345. https://doi.org/10.1016/j.ymssp.2013.01.021 Moghadam NT, Malekmohammadi B. 2025. Assessment of drought change trends and watershed health using a reliability-resilience-vulnerability framework in mountainous watersheds. Current Research in Environmental Sustainability. 9: 100285. https://doi.org/10.1016/j.crsust.2025.100285 Moghaddasi P, Kerachian R, Sharghi S. 2022. A stakeholder-based framework for improving the resilience of groundwater resources in arid regions. Journal of Hydrology. 609: 127737. https://doi.org/10.1016/j.jhydrol.2022.127737 Moshir Panahi D, Kalantari Z, Ghajarnia N, Seifollahi-Aghmiuni S, Destouni G. 2020. Variability and change in the hydro-climate and water resources of Iran over a recent 30-year period. Scientific Reports. 10(1): 7450. https://doi.org/10.1038/s41598-020-64089-y Nair AS, Indu J. 2021. Assessment of groundwater sustainability and identifying factors inducing groundwater depletion in India. Geophysical Research Letters. 48(3): e2020GL087255. https://doi.org/10.1029/2020GL087255 Narany TS, Sefie A, Aris AZ. 2018. The long-term impacts of anthropogenic and natural processes on groundwater deterioration in a multilayered aquifer. Science of The Total Environment. 630: 931–942. https://doi.org/10.1016/j.scitotenv.2018.02.190 Newton B, Spence C. 2023. JAMES BUTTLE REVIEW: A resilience framework for physical hydrology. Hydrological Processes. 37(7): e14926. https://doi.org/10.1002/hyp.14926 Noori R, Maghrebi M, Jessen S, Bateni SM, Heggy E, Javadi S, AghaKouchak A. 2023. Decline in Iran’s groundwater recharge. Nature Communications. 14(1): 6674. https://doi.org/10.1038/s41467-023-42411-2 Peterson GD. 2002. Estimating resilience across landscapes. Conservation Ecology. 6(1): 17. https://doi.org/10.5751/ES-00375-060117 Qi M, Feng ML, Sun T, Yang W. 2016. Resilience changes in watershed systems: A new perspective to quantify long-term hydrological shifts under perturbations. Journal of Hydrology. 539: 281–289. https://doi.org/10.1016/j.jhydrol.2016.05.039 Rhode MM. 2023. Doctoral dissertation, State University of New York College of Environmental Science and Forestry. 531 pp. https://experts.esf.edu/esploro/outputs/doctoral/Groundwater-for-Ecosystems-Reliance-Resilience-and/99945582504826 Riaz MT, Riaz MT, Rehman A, Bindajam AA, Mallick J, Abdo HG. 2024. An integrated approach of support vector machine (SVM) and weight of evidence (WOE) techniques to map groundwater potential and assess water quality. Scientific Reports. 14: 22345. https://doi.org/10.1038/s41598-024-76607-3 Richey AS, Thomas BF, Lo MH, Famiglietti JS, Swenson S, Rodell M. 2015. Uncertainty in global groundwater storage estimates in a Total Groundwater Stress framework. Water Resources Research. 51(7): 5198–5216. https://doi.org/10.1002/2015WR017351 Roos T, Myllymaki P, Tirri H. 2002. A statistical modeling approach to location estimation. IEEE Transactions on Mobile Computing. 1(1): 59–69. https://doi.org/10.1109/TMC.2002.1011059 Safari M, Doulati Ardejani F, Maghsoudy S. 2023. A comparative and coupled study of the application of Dempster-Shafer, fuzzy overlay and FAHP methods for groundwater potential mapping in a fractured medium of a mine site. Earth Science Informatics. 16(2): 1741–1764. https://doi.org/10.1007/s12145-023-01006-7 Saito L, Christian B, Diffley J, Richter H, Rohde MM, Morrison SA. 2021. Managing groundwater to ensure ecosystem function. Groundwater. 59(3): 322–333. https://doi.org/10.1111/gwat.13089 Scanlon BR, Fakhreddine S, Rateb A, de Graaf I, Famiglietti J, Gleeson T, Zheng C. 2023. Global water resources and the role of groundwater in a resilient water future. Nature Reviews Earth and Environment. 4(2): 87–101. https://doi.org/10.1038/s43017-022-00378-6 Sharifi A, Yamagata Y. 2018. Resilience-oriented urban planning. In Resilience-oriented urban planning: theoretical and empirical insights, Cham: Springer International Publishing. pp. 3–27. https://doi.org/10.1007/978-3-319-75798-8_1 Shivakoti BR, Villholth KG, Pavelic P, Ross A. 2019. Strategic use of groundwater-based solutions for drought risk reduction and climate resilience in Asia and beyond. Contributing paper to Global Assessment Report on Disaster Risk Reduction (GAR 2019). Geneva, Switzerland: United Nations Office for Disaster Risk Reduction. Shrestha S, Kafle R, Pandey VP. 2017. Evaluation of index-overlay methods for groundwater vulnerability and risk assessment in Kathmandu Valley, Nepal. Science of The Total Environment. 575: 779–790. https://doi.org/10.1016/j.scitotenv.2016.09.141 Singha K, Navarre-Sitchler A. 2022. The importance of groundwater in critical zone science. Groundwater. 60(1): 27–34. Soltani E, Soltani A, Alimagham M, Dehghani AA. 2023. Water conservation in cropping systems through the reduction of soil evaporation: a case study of Iran. Arabian Journal of Geosciences. 16(3): 198. https://doi.org/10.1007/s12517-023-11296-2 Sun D, Gu J, Chen J, Xia X, Chen Z. 2022. Spatiotemporal differentiation and influencing factors of urban water supply system resilience in the Yangtze River Delta urban agglomeration. Natural Hazards. 114(1): 101–126. https://doi.org/10.1007/s11069-022-05381-0 Sweya LN, Wilkinson S, Kassenga G, Lugomela G. 2021. Development of a tool for measuring resilience of water supply systems in Tanzania: Technical dimension. Journal of Water Resources Planning and Management. 147(2): 04020107. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001317 Theodoridou PG, Varouchakis EA, Karatzas GP. 2017. Spatial analysis of groundwater levels using fuzzy logic and geostatistical tools. Journal of Hydrology, 555: 242–252. https://doi.org/10.1016/j.jhydrol.2017.10.027 Tzampoglou P, Ilia I, Karalis K, Tsangaratos P, Zhao X, Chen W. 2023. Selected worldwide cases of land subsidence due to groundwater withdrawal. Water. 15(6): 1094. https://doi.org/10.3390/w15061094 Vaezi A, Jones S, Asgary A. 2023. Integrating resilience into risk matrices: A practical approach to risk assessment with empirical analysis. Journal of Risk Analysis and Crisis Response. 13(4): 252-272. https://doi.org/10.54560/jracr.v13i4.411 Vu TD, Ni CF, Li WC, Truong MH, Hsu SM. 2021. Predictions of groundwater vulnerability and sustainability by an integrated index-overlay method and physical-based numerical model. Journal of Hydrology. 596: 126082. https://doi.org/10.1016/j.jhydrol.2021.126082 Wang H, Herrmann M, Schroeter SA, Zerfaß C, Lehmann R, Lehmann K, Küsel K. 2025. Groundwater microbiomes balance resilience and vulnerability to hydroclimatic extremes. Communications Earth and Environment. 6(1): 683. https://doi.org/10.1038/s43247-025-02680-9 Wei D, Deng X, Zhang X, Deng Y, Mahadevan S. 2013. Identifying influential nodes in weighted networks based on evidence theory. Physica A: Statistical Mechanics and its Applications. 392(10): 2564–2575. https://doi.org/10.1016/j.physa.2013.01.054 Zeng P, Sun F, Liu Y, Che Y. 2020. Future river basin health assessment through reliability-resilience-vulnerability: Thresholds of multiple dryness conditions. Science of The Total Environment. 741: 140395. https://doi.org/10.1016/j.scitotenv.2020.140395 Zhang Q, Ning Z, Ding X, Wu J, Wang Z, Tsangaratos P, Liu J, Chen W. 2024. Hybrid integration of bagging and decision tree algorithms for landslide susceptibility mapping. Water. 16(5): 657. https://doi.org/10.3390/w16050657 Zoghi M, Amiri MJ. 2025. Detecting the resilience, resistance, and elasticity concepts in the anthropocene epoch in complex urban ecosystems (Hyrcanian forests in Gorgan City-Iran). Tropical Ecology. pp. 1–15. https://doi.org/10.1007/s42965-025-00375-y | ||
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