بررسی تأثیر سناریوهای عدم قطعیت در مدلسازی چشمه‌های آب زیرزمینی با استفاده از مدل مکسنت در حوزه‌ی آبخیز نمرود، تهران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران

2 گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران

3 دانشیار گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه،ایران

چکیده

 
در پژوهش حاضر نقشه پتانسیل چشمه؜های آب زیرزمینی با استفاده از مدل بیشینه‌ی بی‌نظمی (مکسنت) به عنوان معیار در حوزه آبخیز نمرود، استان تهران تهیه شد. در این مطالعه تاثیر سه سناریوی مختلف عدم قطعیت از جمله تعداد نقاط حضور (100،500،800 چشمه)، وضوح رستری (100،50،30) و اندازه نمونه (10/90، 20/80، 30/70، 50/50) روی نقشه؜ی پیش؜بینی شده بررسی شد. ابتدا 18 عامل مؤثر در ظهور چشمه؜ها شامل: سازندهای سنگ؜شناسی، فاصله از گسل، تراکم گسل، طبقات ارتفاعی، درصد شیب، جهت شیب، فاکتور طول شیب، نقشه؜های انحنا، فاصله از آبراهه، تراکم آبراهه، فاصله از جاده، شاخص رطوبت توپوگرافی، موقعیت شیب نسبی، شاخص توان جریان، بافت خاک، شاخص زبری سطح و پوشش کاربری اراضی انتخاب شدند و نقشه آن‌ها در سامانه ArcGIS10.5 و SAGA تهیه گردید. پس از بررسی هم‌خطی و طبقه‌بندی لایه؜های مؤثر، سپس فراوانی وقوع چشمه در هر طبقه با استفاده از روش نسبت فراوانی به دست آمد، که این روش میزان دقیقی از وزن مربوط به هر طبقه را محاسبه می‌کند. برای ارزیابی عملکرد مدل؜های مذکور از منحنی مشخصه عملکرد نسبی (ROC) استفاده شد. نتایج نشان داد که ترکیب سناریوهای وضوح رستری: 30-تعداد نقاط: 100-اندازه نمونه: 20/90 با داشتن بالاترین مقدار AUC ( 953/0 در مرحله آموزش  927/0 در مرحله اعتبارسنجی) دارای عملکرد بهتر و توانایی پیش؜بینی بالاتری نسبت به ترکیب سناریوهای دیگر بودند. علاوه بر این، حدود 14/9 درصد از منطقه مورد مطالعه، پتانسیل بالا و خیلی بالایی به آب زیرزمینی داشته است. براساس ترکیب سناریوی برتر، عوامل فاصله از آبراهه، موقعیت شیب نسبی، تراکم زهکشی و شاخص رطوبت توپوگرافی به ترتیب با مقادیر 3/17، 2/14 ،8/13 و 2/10درصد از عوامل اصلی کنترل مکانی در منطقه مورد مطالعه هستند که در وقوع چشمه موثر هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigation the effect of diffrent number of presence points, raster resolution and sample size scenarios on the groundwater potential map prediction using data mining model in Namroud catchment, Tehran

نویسندگان [English]

  • ali kahrizi 1
  • mohammad ali izadbakhsh 2
  • saeid shabanlou 3
  • fariborz yosefvand 2
1 Master Science Student, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
2 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
3 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
چکیده [English]

In the present study, the potential map of groundwater springs was prepared using the famous and efficient Maxent model as a benchmark in Namroud catchment, Tehran province. In this study, the effect of three different scenarios on the number of presence points (100,500,800 springs), raster resolution (100,50,30) and sample size (10/90, 20/80, 30/70, 50/50) on the predicted map was evaluated. First, 18 factors affecting the emergence of springs including lithological formations, the distance from the fault, fault density, elevation classes, slope percentage, slope direction, slope length factor, curvature maps, the distance from the waterway, waterway density, the distance from the road, topographic moisture index, relative slope position, flow power index, soil texture, surface roughness index and land use cover were selected and their maps were prepared in the ArcGIS10.5 and SAGA systems. After the correlation test and classification of the effective layers, the percentage and frequency of groundwater potential in each class were obtained using the frequency ratio method, which calculates the exact weight of each class. The relative operating characteristic curve (ROC) was used to evaluate the performance of these models. The results showed that the combination of raster resolution scenarios: 30-number of points: 100-sample size: 90/20 with the highest AUC (0.953 in training phase and 0.927 in validation phase) has goodness-of-fit and prediction accuracy compared to the combinations of other scenarios. In addition, about 9.14% of the study area had high and very high potential for groundwater. Based on the combination of the best scenario, the factors of distance from the waterway, relative slope position, drainage density and topographic moisture index with values of 17.3, 14.2, 13.8 and 10.2%, respectively, are the main factors of spatial control in the study area influencing the spring occurrence.

کلیدواژه‌ها [English]

  • Groundwater potential
  • spring
  • frequency ratio
  • Maxent model
  • relative operating characteristic curve

Investigation the effect of diffrent number of presence points, raster resolution and sample size scenarios on the groundwater potential map prediction using data mining model in Namroud catchment, Tehran

EXTENDED ABSTRACT

 

Introduction

Groundwater is one of the most important sources of fresh water for humans. With the significant increase in agricultural, industrial and domestic activities in recent years, the need for quality water to meet growing needs has increased. In order to meet this growing demand, groundwater has a higher priority than surface water due to its low pollution potential and wide and balanced distribution. Groundwater is not an unlimited resource, so the planning of its consumption should be based on understanding the behavior of groundwater systems by ensuring its sustainable use. As the demand for fresh groundwater in the world is increasing, the mapping of potential groundwater source areas has become an important tool for the successful implementation of groundwater conservation and management programs.

 

Materials and Methods

In the present study, the potential map of groundwater springs was prepared using the famous and efficient Maxent model as a benchmark in Namroud catchment, Tehran province.

 

Results and discussion

In this study, the effect of three different scenarios on the number of presence points (100,500,800 springs), raster resolution (100,50,30) and sample size (10/90, 20/80, 30/70, 50/50) on the predicted map was evaluated. First, 18 factors affecting the emergence of springs including lithological formations, the distance from the fault, fault density, elevation classes, slope percentage, slope direction, slope length factor, curvature maps, the distance from the waterway, waterway density, the distance from the road, topographic moisture index, relative slope position, flow power index, soil texture, surface roughness index and land use cover were selected and their maps were prepared in the ArcGIS10.5 and SAGA systems. After the correlation test and classification of the effective layers, the percentage and frequency of groundwater potential in each class were obtained using the frequency ratio method, which calculates the exact weight of each class. The relative operating characteristic curve (ROC) was used to evaluate the performance of these models. The results showed that the combination of raster resolution scenarios: 30-number of points: 100-sample size: 90/20 with the highest AUC (0.953 in training phase and 0.927 in validation phase) has goodness-of-fit and prediction accuracy compared to the combinations of other scenarios. Then, this map is classified into five categories: very low, low, medium, high and very high. Based on the combination of the best scenario, the factors of distance from the stream, relative slope position, drainage density and topographic humidity index are respectively identified as the most important environmental factors affecting the spatial prediction of spring occurrence in the studied area. The results indicate that the factors affecting the determination of groundwater potential in different regions are different due to different climatic conditions, soil science, vegetation, etc.

 

Conclusion

In addition, about 9.14% of the study area had high and very high potential for groundwater. Based on the combination of the best scenario, the factors of distance from the waterway, relative slope position, drainage density and topographic moisture index with values of 17.3, 14.2, 13.8 and 10.2%, respectively, are the main factors of spatial control in the study area influencing the spring occurrence.

Arkoprovo, B., Adarsa, J., Shashi Prakash, S. (2012). Delineation of groundwater potential zones using satellite remote sensing and geographic information techniques: a case study from Ganjam district, Orissa, India. Research Journal of Recent Sciences, 9, 59–66.
Austin, M. (2007). Species distribution models and ecological theory, a critical assessment and some possible new approaches. Ecological Modelling, 200(1),1–19
Ariyanto, A.C. (2015). Mapping of possible corridors for Javan Leopard (Panthera pardus ssp. melas) between Gunung Merapi and Gunung Merbabu National Parks, Indonesia. Doctoral dissertation, University of Twente.
Azareh, A., Rahmati, O., Rafiei-Sardooi, E., Sankey, J.B., Lee, S., Shahabi, H., and Ahmad, B.B. (2019). Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Science of the Total Environment, 655, 684-696.
Baldwin, R.A. (2009). Use of maximum entropy modeling in wildlife research. Entropy 11(4), 854–866
Beven, K., and Freer, J. (2001). A dynamic TOPMODEL. Hydrological Process, 15(10), 1993–2011.
Böhner, J., and Selige, T. (2006). Spatial prediction of soil attributes using terrain analysis and climate regionalisation. Gottinger Geographische Abhandlungen, 115, 13-28.
Bhattacharya, A.K. (2010). Artificial ground water recharge with a special reference to India. International Journal of Research and Reviews in Applied 4, 214–221.
Bera, K., and Bandyopadhyay, J. (2012). Ground water potential mapping in Dulung watershaed using remote sensing and GIS techniques, West Bangal, India. International Journal of Scientific and Research Publication, 2(12), 1–7.
Conforti, M., Pascale, S., Robustelli, G., and Sdao, F. (2014). Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turblo River catchment (northern Calabria, Italy). Catena, 113, 236-250.
Dehnavi, A., Aghdam, I.N., Pradhan, B., and Varzandeh, M.H.M. (2015). A new hybride model using step- wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148.
Demir, G., Aytekin, M., and Akgun, A. (2015). Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey). Arabian Journal of Geosciences, 8(3), 1801-1812.
Daoud, J.I. (2017). Multicollinearity and regression analysis. J. Phy, Conference Series. 949(1), 012009). IOP Publishing.
Davoodi Moghaddam, D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., and Pradhan, B. (2013). Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Jornal of Geoscience, 8(2), 913–929.
Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., and Ryu, I.C. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1), 135–165.
Deepa, S., Venkateswaran, S., Ayyandurai, R. (2016). Groundwater recharge potential zones mapping in upper Manimuktha Sub basin Vellar river Tamil Nadu India using GIS and remote sensing techniques. Model Earth Syst Environ 2(137). https ://doi. org/10.1007/s4080 8-016-0192.
Felicĺsimo, Á., Cuartero, A., Remondo, J., and Quirόs, E. (2013). Mapping landslide susceptibility with logistiv regression, multiple adaptive regression splines, classification and regression tress, amd maximum entropy methods: a comparative study. Landslides, 10, 175-189
Jungerius, P.D., Matundura, J., and Van de Ancker, J.A.M. (2002). Road construction and gully erosion in West Pokot, Kenya. Earth Surf Process Landf, 27, 1237–1247.
Jaiswal RK, Mukherjee S, Krishnamurthy J, Saxena R (2003) Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development—an approach. Int J Remote Sens 24(5):993–1008.
Israil M, Al-hadithi M, Singhal DC, Kumar B, Rao MS, Verma K (2006) Groundwater resources evaluation in the Piedmont zone of Himalaya, India, using isotope and GIS technique. J Spat Hydrol 6(1):34–38
Greene, W.H. (2000). Econometric Analysis (4th Edition), Upper Saddle River, NJ: Prentice Hall.
Godebo TR (2005) Application of remote sensing and GIS for geological investigation and groundwater potential zone identification, Southeastern Ethiopian Plateau, Bale Mountains and the surrounding areas. ADDIS ABABA UNIVERSITY, Dissertation
Gomez, H., and Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in jabonosa River Basin, Venezuela. Engineering Geology, 78(1), 11-27.
Gutie´rrez, A.G., Schnabel, S., Contador, J.F.L. (2009). Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecological Modelling, 220, 3630–3637.
Golkarian, A., and Rahmati, O. (2018). Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran. Environmental earth sciences,77(10), 1-20.
Ghosh, S. and Carranza, E.J.M. (2010). Spatial analysis of mutual fault/fracture and slope controls on rock sliding in Darjeeling Himalaya, India. Geomorphology 122, 1- 24.
Gallardo-Cruz, J.A., Pérez-García, E.A., and Meave, J.A. (2009). β-Diversity and vegetation structure as influenced by slope aspect and altitude in a seasonally dry tropical landscape. Landsc Ecol, 24, 473–482.
Geroy, I.J., Gribb, M.M., Marshall, H.P., Chandler, D.G., Benner, S.G., and McNamara, J.P. (2011). Aspect influences on soil water retention and storage. Hydrol Processes, 25, 3836–3842.
Javidan, N., Kavian, A., Pourghasemi, H.R., Conoscenti, C., and Jafarian, Z. (2020). Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios. Water, 11(11), 231-239.
Jaime, R., Alcjntara, J.M., Bastida, J.M., Rey, P.J. (2015). Complex patterns of environmental niche evolution in Iberian columbines (genus Aquilegia, Ranunculaceae). Journal of Plant Ecology, 8(5), 457–467
Kornejady, A., Ownegh, M., and Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152, 144-162.
Lee, S., and Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4, 33-41.
Lee, S., Song, K.Y., and Kim, Y. (2012). Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeol J 20, 1511–1527.
Moghaddam, D.D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., and Pradhan, B. (2015). Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences, 8(2), 913-929.
Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Soleiman Wan Nor, A., and Ramli, M. F. (2012), Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS, Arabian Journal of Geosciences, 7, 711-724.
Mancini, F., Ceppi, C., and Ritrovato, G. (2010). GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Natural Hazards and Erth System Sciences, 10(9): 1851-1859.
Moore, I.D., Grayson, R.B., and Ladson, A.R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrology Process, 5, 3-30.
Meinhardt, M., Fink, M., and Tunschel, H. (2015). Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology, 234, 80–97.
Madrucci V, Taioli F, Cesar de Araujo C (2008) Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, Sao Paulo State, Brazil. J Hydrol 357:153–173
Naghibi, S. A., Pourghasemi, H. R., Pourtaghi, Z. S., and Rezaei, A. (2015). Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics, 8(1), 171-186.
Nguyen, P. T., Ha, D.H., Avand, M., Jaafari, A., Nguyen, H.D., Al-Ansari, N., and Ho, L.S. (2020), Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences, 10(7), 2469-2478.
Naghibi, S.A., Pourghasemi, H.R., Dixon, B. (2016) Groundwater spring potential using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess. doi:10.1007/s10661-015-5049-6
Oh, H.J., Kim, Y.S., Choi, J.K., Park, E., and Lee, S. (2011). GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399(3-4), 158-172.
Ozdemir, A. (2011). GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of Hydrology 411, 290-308.
Pearce, J., Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model, 133(3), 225–245
Park, N.W. (2015). Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, 73(3), 937-949.
Pourtaghi, Z.S., and Pourghasemi, H.R. (2014). GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), 643-662.
Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques, Central European Journal of Geosciences, 1(1), 120–129.
Pourghasemi, H.R., and Rossi, M. (2017). Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 130(1–2), 609–633.
Pourghasemi, H.R., Kornejady, A., Kerle, N., and Shabani, F. (2020). Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena, 187, 104364-76.
Riley, S.J., Degioria, S.D., and Elliot, R. (1999). Index that quantifies topographic heterogeneity. intermountain Journal of sciences, 5(1-4), 23-27.
Rahmati, O., Pourghasemi, H.R., and Melesse, A.M. (2016). Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137, 360-372.
Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H.R., and Feizizadeh, B. (2017). Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. Science of The Total Environment, 579, 913-927.
seyed ali, S., rahimi, M., Dastourani, J., and Khosroshahi, M. (2016). Trend Analysis of Hydroclimatological Parameters and Detection of Manageral Changes in Water Resources Conditions of Hablerood Watershed, Iranian Journal of Range and Desert Research, 23(3), 555-566.
Shahin, K.A., and Hassan, N. (2000). Sources of shared variability among body shape characters at marketing age in New Zealand White and Egyptian rabbit breeds. Annales de Zootechnie, 49, 435- 445.
Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., and Rahmati, O. (2018). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto international, 33(9), 927-941.
Sidel, R.C., and Ochiai, H. (2006). Landslides: Processes, Prediction, and Land use, Water Resource Monograph: 18, AGU books, ISSN: 0170-9600.
Shirzadi, A., Saro, L., Hyun-Joo, O.h., and Chapi, K. (2012). A GIS-based logistic regression model in rock fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazard. 64, 1639-1656.
Shirzadi, A., Soliamani, K., Habibnejhad, M., Kavian, A., Chapi, K., Shahabi, H., ... and Tien Bui, D. (2018). Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors, 18(11), 3777.
Shary, P.A., Sharaya, L.S., and Mitusov, A.V. (2002). Fundamental quantitative methods of land surface analysis. Geoderma, 107 (1), 1–32.
Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656 [Mathematical Reviews. (MathSciNet), MR10, 133e]
Thuiller W, Richardson DM, Pyšek P, Midgley GF, Hughes GO, Rouget M (2005) Niche-based modeling as a tool for predicting
the risk of alien plant invasions at a global scale. Glob Chang Biol 11(12):2234–2250
-Tehrany M.S., Pradhan B., Mansor S. and Ahmad N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91-101.
Vandekerckhove, L., Poesen, J., and Govers, G. (2003). Medium-term gully headcut retreat rates in Southeast Spain determined from aerial photographs and ground measurements. Catena, 50,  329-352.
Vorpahl, P., Elsenbeer, H., Mӓrker, M., and Schröder, B. (2012). How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239, 27-39.
Williams RJ (2010) Simple MaxEnt models explain food web degree distributions. Theor Ecol 3(1), 45–52.
Walter, S.D. (2002). Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med. 21, 1237–1256.
Xu, C., Dai, F., Xu, X., and Lee, Y.H. (2012). GIS-based support vector mechine modeling of earthquake-triggered landslide susceptibility in the jianjiand River watershed, China. Geomorphology, 145, 70-80.
Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena, 72, 1–12.
Yessilnacar, E.K. (2005). The application of computational intelligence of landslide susceptibility mapping in Turkey. Ph. D Thesis Department of Geomatics the University of Melbourne. 423 pages.
Zabihi, M., Pourghasemi, H.R., Pourtaghi, Z. S., and Behzadfar, M. (2016). GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environmental Earth Sciences, 75(8), 665-674.