Assessment and Prediction of Rill erosion by using data Mining and remote sensing data (A case study: Khasoyuh watershed in Fars Provinceو, Iran)
Document Type : Original Article
Authors
1
Isfahan university
2
Ph.D. student of Geomorphology, University of Isfahan
10.22034/gmpj.2024.411467.1451
Abstract
Abstract
Water erosion is one of the most type of land degradation and desertification in many different climates in the world. Among the different forms of water erosion, rill erosion is one of the most important. This type of water erosion can cause one of the main source of sediments. Our study area is located in Fars province in South of Iran that is effected by difference type of soil loss, same as gully, rill and sheet erosion. For prediction of rill erosion in this research we first digitized the points of rill areas with using GE, filed survey and aerial photos. Different index has been appalled as independent variable, same as, topographic indices, LULC, climate, soil. The topographic Indices have been generated from Tan-DEM-X with 12.5 m in SAGA-GIS. In the next step, after preparing all variables, we have use the Maxent model for prediction of the rill erosion in our study area. In this study we have divided the data of rill erosion points into two categories, its meaning 30% for testing and 70% of the data to training the model. The model used was finally introduced the most effective indicators. In order to validate the model for the zonation of rill erosion, the studied area was evaluated using (AUC) and the area under the curve (ROC) of the curve. The results of this research showed that the slope index and land use and distance from the rivers are the most effective indicators in creating rill erosion.
Key words: Rill erosion, Maxent model, zoning, Khasoyuh watershed, GIS
Assessment and Prediction of Rill erosion by using data Mining and GIS (A case study: Khasoyuh watershed in Fars Province, Iran)
Extended abstract
Introduction
Soil erosion is a serious threat to human well-being and life. According to the importance and the way of formation, water erosion is divided into water erosion, Rill erosion includes about 70 to 50% of four groups including erosion, watercourses, Rill and all soil erosion. In many researches, this type of erosion The most important process of sediment production and as a result soil loss has been reported, Rill erosion as the initial stage of hydrological erosion is the importance of water in sloping fields, but it also causes the loss of surface It has many. On the one hand, furrows not only cause significant loss of soil soil nutrients and as a result causes land degradation and pollution of downstream rivers.
Methodology
First, the location of the Rill through field surveys and satellite images and aerial photos using physiographic maps software (height, slope, slope direction, slope length, relative slope, vertical distance to the network channel, topographic humidity, convergence, length Curvature, flow power, cumulative flow, distance from the waterway, channel depth) were prepared using DEM height digital model data with a resolution of 30 meters in (GIS-SAGA) software Vegetation and land use parameters using Landsat 8 and Sentinel 2 satellite images for 2021 It was prepared in the software (GIS Arc 10.8), the map of soil and geological indicators is also in the software 10.8 ArcGIS software was prepared, after preparing the map of the effective indicators of the model, it was implemented. The basis of the selection of factors It has been investigated in various studies of other researchers about Rill erosion in different parts of the world. In this study, the maximum entropy model is used, which is one of the machine learning technologies (MLTs) that has a high spatial prediction ability in the field. has various environmental sciences. also to check the validity of the final model, the area under the curve index (AUC) for the ROC curve was used. If the level below be. If the curve is more than 0.9, the recognition power of the model is considered excellent.
Results
In this research, to determine the impact of each variable on the amount of Rill erosion in the region, using the method of percentage participation of each variable It has been used using Maxnet output. The result of this research shows the slope index is one of the most important variable for event of rill erosion in our study area. In the other hand, the area with high slope has more potential for this type of soil loss. The second more important variable is land use land cover in the study area, because area with low vegetation are more prone for eroding of soil by water. In total we can mention the topography condition is the most important criteria for the rill erosion in this watershed.
Dissection:
The result of the area under the curve (AUC) in this study was equal to 0.947, which indicates that the prediction of the model in zoning the furrow erosion areas is very high, considering the four influencing factors on furrow erosion. including the slope, land use, vegetation and distance from the waterway, the model analysis in this research showed that the slope index has the most impact and importance, and there is an inverse relationship between the slope and areas prone to gully erosion, the lower the slope, the level of gully erosion in the area more than.
Water erosion is one of the most type of land degradation and desertification in many different climates in the world. Among the different forms of water erosion, rill erosion is one of the most important. This type of water erosion can cause one of the main source of sediments. Our study area is located in Fars province in South of Iran that is effected by difference type of soil loss, same as gully, rill and sheet erosion. For prediction of rill erosion in this research we first digitized the points of rill areas with using GE, filed survey and aerial photos. Different index has been appalled as independent variable, same as, topographic indices, LULC, climate, soil. The topographic Indices have been generated from Tan-DEM-X with 12.5 m in SAGA-GIS. In the next step, after preparing all variables, we have use the Maxent model for prediction of the rill erosion in our study area. In this study we have divided the data of rill erosion points into two categories, its meaning 30% for testing and 70% of the data to training the model. The model used was finally introduced the most effective indicators. In order to validate the model for the zonation of rill erosion, the studied area was evaluated using (AUC) and the area under the curve (ROC) of the curve. The results of this research showed that the slope index and land use and distance from the rivers are the most effective indicators in creating rill erosion.
Key words: Rill erosion, Maxent model, zoning, Khasoyuh watershed, GIS
Assessment and Prediction of Rill erosion by using data Mining and GIS (A case study: Khasoyuh watershed in Fars Province, Iran)
Extended abstract
Introduction
Soil erosion is a serious threat to human well-being and life. According to the importance and the way of formation, water erosion is divided into water erosion, Rill erosion includes about 70 to 50% of four groups including erosion, watercourses, Rill and all soil erosion. In many researches, this type of erosion The most important process of sediment production and as a result soil loss has been reported, Rill erosion as the initial stage of hydrological erosion is the importance of water in sloping fields, but it also causes the loss of surface It has many. On the one hand, furrows not only cause significant loss of soil soil nutrients and as a result causes land degradation and pollution of downstream rivers.
Methodology
First, the location of the Rill through field surveys and satellite images and aerial photos using physiographic maps software (height, slope, slope direction, slope length, relative slope, vertical distance to the network channel, topographic humidity, convergence, length Curvature, flow power, cumulative flow, distance from the waterway, channel depth) were prepared using DEM height digital model data with a resolution of 30 meters in (GIS-SAGA) software Vegetation and land use parameters using Landsat 8 and Sentinel 2 satellite images for 2021 It was prepared in the software (GIS Arc 10.8), the map of soil and geological indicators is also in the software 10.8 ArcGIS software was prepared, after preparing the map of the effective indicators of the model, it was implemented. The basis of the selection of factors It has been investigated in various studies of other researchers about Rill erosion in different parts of the world. In this study, the maximum entropy model is used, which is one of the machine learning technologies (MLTs) that has a high spatial prediction ability in the field. has various environmental sciences. also to check the validity of the final model, the area under the curve index (AUC) for the ROC curve was used. If the level below be. If the curve is more than 0.9, the recognition power of the model is considered excellent.
Results
In this research, to determine the impact of each variable on the amount of Rill erosion in the region, using the method of percentage participation of each variable It has been used using Maxnet output. The result of this research shows the slope index is one of the most important variable for event of rill erosion in our study area. In the other hand, the area with high slope has more potential for this type of soil loss. The second more important variable is land use land cover in the study area, because area with low vegetation are more prone for eroding of soil by water. In total we can mention the topography condition is the most important criteria for the rill erosion in this watershed.
Dissection:
The result of the area under the curve (AUC) in this study was equal to 0.947, which indicates that the prediction of the model in zoning the furrow erosion areas is very high, considering the four influencing factors on furrow erosion. including the slope, land use, vegetation and distance from the waterway, the model analysis in this research showed that the slope index has the most impact and importance, and there is an inverse relationship between the slope and areas prone to gully erosion, the lower the slope, the level of gully erosion in the area more than.
Keywords
احمدی، حسن (1378). ژئومورفولوژی کاربردی، جلداول، فرسایش آبی، انتشارات دانشگاه تهران، 634 ص.
پژوهش، م.، گرجی، م.، طاهری، م.، سرمدیان، ف.، مجمدی، ج.، صمدی، ح. (1390). اثر کاربری اراضی مختلف حوضه سد زایندهرود علیا در تولید رسوب با استفاده از GIS . مجله پژوهش آب ایران، 5 (2): 143-152.
رفاهی، ح. ق.(1388). فرسایش آبی وکنترل آن، انتشارات دانشگاه تهران، تهران،ذصص۶۷۲-۱.
عرب خدری، م. (1393). مروری بر عوامل مؤثر بر فرسایش آبی خاک در ایران، نشریه مدیریت اراضی، دوره ۲، شماره ۱،صص۲۶-۱۷.
خرم، م.، موسوی نسب، ز. (1392). بررسی هیدروژئو شیمی منابع آب زیر زمینی دشت خسویه – حاجی آباد، زرین دشت فارس، انجمن زمین شناسی ایران.
واعظی، ع و محمدی، الف. (1400). الگوی تغییرات زمانی تولید رواناب و فرسایش شیاری در خاکها و درجات مختلف شیب. علوم آب و خاک. ۱۴۰۰; ۲۵ (۴) :۱۹-۳۱.
واعظی، ع.، قره داغلی، ح.، مرزوان، س. (1394). بررسی نقش شیب و ویژگی های خاک در ایجاد فرسایش شیاری در دامنه ها ( مطالعه موردی: حوضه آبخیز تهم چای، شمال غرب زنجان ). نشریه پژوهش های حفاظت آب و خاک ،23(14): 83-100.
ذاکری نژاد، ر. (1399). ارزیابی مدلهای رقومی ارتفاع جهت تهیه نقشه پتانسیل فرسایش خندقی با استفاده از مدل مکسنت و سامانه اطالعات جغرافیایی در حوضه آبخیز سمیرم، جنوب استان اصفهان؛ نشریه سنجش از دور سامانه اطالعات جغرافیایی در منابع طبیعی؛11،(3)،122-106.
ذاکری نژاد، ر.، الوندی، پ. (1402). پیشبینی فرسایش خندقی با استفاده از دادههای X-TanDEM و مدل حداکثرآنتروپی )مطالعه موردی: حوضه آبخیز خسویه(. پژوهش های فرسایش محیطی، 49 (1): 13.
Anguiano-Morales, M., Corral-Martínez, L.F., Trujillo-Schiaffino, G., Salas-Peimbert, D.P., García-Guevara, A.E. (2018). Topographic investigation from a low altitude unmanned aerial vehicle. Oppt. Lasers. Eng. 110, 63–71. https://doi.org/10.1016/j. optlaseng.2018.05.015
Bagnold, R. A. (1966). An approach to the sediment transport problem from general physics. US government printing office, 10.3133/pp422I.
Battany, M. and Grismer, M. (2000). Rainfall runoff and erosion in Napa Valley vineyards: effects of slope, cover and surface roughness. Hydrological Processes. 14(7): 1289-1301.
Bai, S.B., Wang, J., Lü, G.N., Zhou, P.G., Hou, S.S. and Xu, S.N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1-2), pp.23-31. doi.org/10.1016/S0167-1987(02)00045-4.
Ban, Y.Y., Lei, T.W. (2022). Mathematical method for physics-based rill erosion process using detachment and transport capacities. Sci Rep 12, 4812 doi.org/10.1038/s41598-022-08512-6.
Bruno, C., Di Stefano C., Ferro V. (2008). Field investigation on rilling in the experimental Sparacia area, South Italy. Earth Surf Process Landf 33:263 279. https://doi.org/10.1002/esp.1544
Borrelli, P., Robinson, D.A., Fleischer, L.R., Lugato, E., Ballabio, C. et al. (2017). An assessment of the global impact of the 21st century land use change on soil erosion. Nat. Commun. 8, 1–13, DOI:10.1038/s41467-017-02142-7.
Borrelli, P., Alewell, C., Alvarez, P. (2021). Soil erosion modelling: A global review and statistical analysis, Science of The Total Environment, Volume 780, 146494. doi.org/10.1016/j.scitotenv.2021.146494.
Beullens, J., Van de Velde, D., Nyssen, J. (2014). Impact of slope aspect on hydrological rainfall and on the magnitude of rill erosion in Belgium and northern France. CATENA 114, 129–139. https://doi.org/10.1016/j.catena.2013.10.016.
Cerdan, O., Le Bissonnais, Y., Couturer, A., Bourennane, H., Souchere, V. (2002). Rill erosion on cultivated hillslopes during two eztreme rainfall events in Normandy, France. Soil and Tillage Research. 67: 99-108. doi.org/10.1016/S0167-1987(02)00045-4.
Chen, X.Y., Y.H. Huang, Y. Zhao, B. Mo and H.X. Mi. (2015). Comparison of loess and purple rill erosions measured with volume replacement method. Journal of Hydrology, 530: 476-483, doi.org/10.1016/j.jhydrol.2015.10.001.
Chen, X.Y., Zhao, Y., Mi, H.X., Mo, B. )2016(. Estimating rill erosion process from eroded morphology in flume experiments by volume replacement method. CATENA 136, 135–140. https://doi.org/10.1016/j.catena.2015.01.013.
Hosmer D.W. & Lemeshow S. (2000). Applied Logistic Regression, 2nd Ed. Wiley, Newyork, 392 Pp.
Hieke, F., Schmidt, J. (2013) The effect of soil bulk density on rill erosion - Results of experimental studies. Zeitschrift fur Geomorphologie 57, 245–266. doi.org/ 10.1127/0372-8854/2012/0091.
Liu, C., Fan, H., Jiang, Y., Ma, R., Song, S. (2023). Gully erosion susceptibility assessment based on machine learning-A case study of watersheds in Tuquan County in the black soil region of Northeast China, CATENA, 222, doi.org/10.1016/j.catena. 202
2.106798.
Iro, S., Duru PN., Achalonu CA. (2022). The use of Remote Sensing and GIS Methodology in the Analysis of Urualla Gully Erosion site Imo State Nigeria. J Environ Sci Curr Res. 5, 030.
Kimaro, D.N., Poesen, J., Msanya, B.M., & Deckers, J.A. (2008). Magnitude of soil erosion on the northern slope of the Uluguru Mountains, Tanzania: Interrill and rill erosion. J. Catena., 75:38. doi.org/10.1016/j.catena.2008.04.007.
Khorram, M., Mousavi Nasab, Z. (2013). Hydrogeochemistry of Groundwater Resources in
Khasoyeh Plain - Hajiabad, Zarrin Dasht Fars. Iranian Geological Society. (in Persian)
Khasoyeh Plain - Hajiabad, Zarrin Dasht Fars. Iranian Geological Society. (in Persian)
Lei, T. W., Zhang, Q. W., Yan, L. J., Zhao, J., and Pan, Y. H. (2008). A rational method for estimating erodibility and critical shear stress of an eroding rill. Geoderma, 144(3-4), 628-63
Morgan, R.P.C. (2005). Soil erosion and conservation. Malden, MA: Blackwell Publishing
Moreno-de las Heras, M., Espigares, T., Merino-Martín, L., Nicolau, J.M. (2011). Water-related ecological impacts of rill erosion processes in Mediterranean-dry reclaimed slopes. Journal of Catena, Vol. 84: PP. 114-124.
Masoudi, M & Zakerinejad R. (2010). Hazard assessment of desertification using MEDALUS model in Mazayjan plain, Fars province, Iran. Ecology, Environment and Conservation Journal. 16 (3): 425-430.
Masoudi M., Zakerinejad R. (2011). A new model for assessment of erosion using desertification model of IMDPA in Mazayjan plain, Fars province, Iran. Ecol Environ Conserv 17(3):489–594.
Wilkinson, S. N., Rutherfurd, I.D., Brooks, A.P., Bartley, R. (2023). Achieving change through gully erosion research. Earth Surf. Process. Landforms. 1–9
Maerker, M., Bosino, A., Scopesi, C., Giordani, P., Firpo, M., Rellini, I. (2020). Assessment of calanchi and rill-interrill erosion susceptibility in northern Liguria, Italy: A case study using a probabilistic modelling framework. Geoderma, 371, p.114367https://doi.org/10.1016/j.geoderma.2020.114367.
Ou, X., Hu, Y., Li, X., Guo, S., Liu, B. (2021). Advancements and challenges in rill formation, morphology, measurement and modeling, CATENA, Volume 196,
https://doi.org/10.1016/j.catena.2020.104932
Pournader, M., Ahmadi, H., Feiznia, S., Karimi, H. and Peirovan, H.R. (2018). Spatial prediction of soil erosion susceptibility: An evaluation of the maximum entropy model. Earth Science Informatics, 11(3), pp.389-401.
Planchon, O & Darboux, F. (2002). A fast, simple and versatile algorithm to fill the depressions of digital elevation models. Catena, 46(2): 159-176. doi:https://doi.org/10.1016/S0341-8162(01)00164-3
Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H, Feizizedeh, B. (2019). Assessing the effectiveness of the maximum entropy model to gully erosion susceptibility prediction in the Kashkan-Poldokhtar Watershed https://doi.org/10.22092/ijwmse.2018.118040,
Yan, L. J., Lei, T. W., Zhang, Q. W., Qu, L.Q. (2008). Effects of transport capacity and erodibility on rill erosion processes; A model study using the Finite Element method. Journal of Geoderma, Vol. 146: PP. 114-120 https://doi.org/10.1016/j.geoderma.2008.05.009.
Zakerinejad, R & Märker, M. (2014). Prediction of Gully Erosion Susceptibilities using detailed Terrain Analysis and Maximum Entropy Modeling: A case study in the Mazayejan Plain, Southwest Iran, Geografia Fisicae Dinamica Quaternaria, 37(1): 67-76
Zakerinejad, R., & Maerker, M. (2015). An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran. Natural Hazards, 79: 25-50.
Zakerinejad R, Omran A, Maerker M, Hochschild V (2018). Assessment of gully erosion in relation to lithology in the Southwestern Zagros Mountains, Iran using ASTER data, GIS and stochastic modeling, Geogr FIS DIN QUAT, (ISSN: 0391-9838) (41.2).
Zakerinejad, R., Sommer, C., Hochschild, V., Maerker, M. (2021). Spatial disterbution of water erosion using stochastic modeling in the southern isfahan province, Iran. 2021; Geogr FIS DIN QUAT 44. (2): 203–216.
Zakerienjad R, Masoud M (2020). Quantitative mapping of desertification risk using modified MEDALUS model: a case study in the Mazayejan Plain, Southwest Iran. AUC Geographica 54(2), 232–239.
Zhang, J., Zhou, L. & Huang, D. (2022). Development of rill erosion on bare sloping farmland under natural rainfall conditions. Environ Earth Sci 81, 264https://doi.org/10.1007/s12665-022-10383-z
