نشریه سنجش از دور و GIS ایران

شماره جاری

بر اساس شماره‌های نشریه

بر اساس نویسندگان

بر اساس موضوعات

نمایه نویسندگان

نمایه کلیدواژه ها

درباره نشریه

اهداف و چشم انداز

اعضای هیات تحریریه

اصول اخلاقی انتشار مقاله

بانک ها و نمایه نامه ها

پیوندهای مفید

پرسش‌های متداول

فرایند پذیرش مقالات

اخبار و اعلانات

بررسی ارتباط بین خشکسالی و کاهش کیفیت آب با استفاده از سنجش از دور و روش شبکه‌های عصبی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه سنجش از دور و GIS، دانشگاه تربیت مدرس

2 دانشیار بخش جغرافیا، دانشکدة اقتصاد، مدیریت و علوم اجتماعی، دانشگاه شیراز

چکیده

با توجه به تأثیر خشکسالی در کیفیت و کمّیت آب، هدف از این مطالعه بررسی خشکسالی با استفاده از شاخص‌های خشکسالی و ارتباط آن با میزان کیفیت آب در مناطق شمالی استان فارس ایران است. برای این منظور، شاخص‌های خشکسالی PCI، TVDI، NDVI در سال‌های 2000 تا 2020 استفاده شد. در ادامه، نقشه‌های پهنه‌بندی عناصر آب (Ca، Cl، EC، K، Na، Mg) با استفاده از روش کریجینگ تولید شد. سپس با به‌کارگیری روش شبکه‌های عصبی (MLP)، میزان عناصر آب با استفاده از شاخص‌های خشکسالی پیش‌بینی شد. نتایج نشان داد که با توجه به مقادیر شاخص‌های خشکسالی، روند تغییرات خشکسالی در منطقه از سال 2000 تا 2020 افزایشی بوده و بخش‌های جنوبی منطقه در وضعیت حادتری به‌نسبت دیگر بخش‌ها قرار دارد. نتایج حاصل از نقشه‌های پهنه‌بندی عناصر آب هم نشان داد که در بخش‌های جنوبی، غلظت املاح بیشتر از بخش‌های شمالی است. طبق نتایج حاصل از همبستگی بین شاخص‌های خشکسالی و مقادیر عناصر آب، Ca همبستگی بالایی (820/0 R=) با شاخص TVDI دارد و عناصر Cl، EC، K، Na، Mg نیز دارای همبستگی معنی‌داری (80/0 R>) با شاخص PCI است. نتایج حاصل از روش MLP، برای پیش‌بینی وضعیت کیفیت آب با استفاده از شاخص‌های خشکسالی، نشان داد که در مناطق جنوبی میزان املاح بیشتر و در نتیجه، کیفیت آب کمتر است. میزان دقت مدل در پیش‌بینی عناصر Cl، EC، K، Na، Mg، TH،TDS  با استفاده از شاخص PCI برابر با 85/0 R2= و درمورد عنصر Ca، با استفاده از شاخص TVDI برابر با 71/0 R2= است.

کلیدواژه‌ها

عنوان مقاله [English]

Investigating the Relationship between Drought and Water Quality Reduction Using Remote Sensing and Neural Network Methods

نویسندگان [English]

  • Mehran Shaygan 1
  • Marzieh Mokarram 2

1 Assistant Prof., Dept. of Remote Sensing & GIS, Tarbiat Modares University

2 Associate Prof., Dep. of Geography, Faculty of Economics, Management and Social sciences, Shiraz University

چکیده [English]

Due to the fact that droughts can affect both water quality and quantity, the purpose of this study is to determine the effect of droughts on water quality and quantity in Northern Fars province, Iran, based on drought indicators. The drought indices PCI, TVDI, and NDVI are used to study drought from 2000 to 2020. Also, the kriging method is used to generate zoning maps of elements in water (Ca, Cl, EC, K, Na, Mg). Then, using the neural network (MLP) method, the amount of elements in the water is predicted based on drought indices. Based on the values of the drought indicators, the trend of drought changes in the region is increasing from 2000 to 2020, with the southern areas of the region experiencing a more acute drought than the rest of the region. In addition, the zoning map of the elements in water indicated that salt concentrations are higher in the southern parts than in the northern parts. Correlation between drought indices and the amounts of elements in water showed that Ca has a high correlation (R2= 0.820) with TVDI index, and also Cl, EC, K, Na, and Mg have significant correlations (R > 0.8) with the index. Using drought indicators, MLP results for predicting water quality status show that southern regions have more solutes and lower water quality. Furthermore, the R2 values of the model for predicting the elements Cl, EC, K, Na, Mg, TDS, TH using PCI index equal to 0.85 and for Ca using TVDI index equal to 0.71, which indicates high accuracy.

کلیدواژه‌ها [English]

  • Drought
  • Water quality
  • Remote sensing
  • Neural network method
مراجع
Bai, J.J., Yu, Y. & Di, L., 2017, Comparison between Tvdi and Cwsi for Drought Monitoring in the Guanzhong Plain, China, Journal Of Integrative Agriculture, 16(2), PP. 389-397. Https://Doi.Org/ 10.1016/S2095-3119(15)61302-8.
Bhandari, A.K., Kumar, A. & Singh, G.K., 2012, Feature Extraction Using Normalized Difference Vegetation Index (Ndvi): A Case Study of Jabalpur City, Procedia Technology, 6, PP. 612-621. Https://Doi.Org/ 10.1016/J.Protcy.2012.10.074
Eliasson, J., 2014, The Rising Pressure of Global Water Shortages, Nature 2014 517:7532, 517(7532), PP. 6-6. Https://Doi.Org/10.1038/517006a.
Fallahati, A., Soleimani, H., Alimohammadi, M., Dehghanifard, E., Askari, M., Eslami, F. & Karami, L., 2020, Impacts of Drought Phenomenon on the Chemical Quality of Groundwater Resources in the Central Part of Iran—Application of Gis Technique, Environmental Monitoring And Assessment, 192(1), PP. 1-19. Https://Doi.Org/10.1007/S10661-019-8037-4/Figures/16.
Gao, Z., Gao, W. & Chang, N.B., 2011, Integrating Temperature Vegetation Dryness Index (Tvdi) and Regional Water Stress Index (Rwsi) for Drought Assessment with the Aid of Landsat Tm/Etm+ Images, International Journal Of Applied Earth Observation And Geoinformation, 13(3), PP. 495-503. Https://Doi.Org/10.1016/J.Jag.2010.10.005.
Ghaedi, S., 2021, Anomalies of Precipitation and Drought in Objectively Derived Climate Regions of Iran, Hungarian Geographical Bulletin, 70(2), PP. 163-174. Https://Doi.Org/ 10.15201/Hungeobull.70.2.5.
Heimann, P. & Isaacs, S., 2018, Regression, Developments In Psychoanalysis, PP. 169-197. Https://Doi.Org/10.4324/9780429473661-5.
Khalili, N., Arshad, M., Farajzadeh, Z., Kächele, H. & Müller, K., 2020, Effect of Drought on Smallholder Education Expenditures in Rural Iran: Implications for Policy, Journal Of Environmental Management, 260, 110136. Https://Doi.Org/10.1016/ J.Jenvman.2020.110136.
Khan, S., Gabriel, H.F. & Rana, T., 2008, Standard Precipitation Index to Track Drought and Assess Impact of Rainfall on Watertables in Irrigation Areas, Irrigation and Drainage Systems, 22, PP. 159-177. Https://Doi.Org/10.1007/S10795-008-9049-3.
Kim, J.S., Jain, S., Lee, J.H., Chen, H. & Park, S.Y., 2019, Quantitative Vulnerability Assessment of Water Quality to Extreme Drought in a Changing Climate, Ecological Indicators, 103, PP. 688-697. Https://Doi.Org/10.1016/J.Ecolind.2019.04.052.
Krak, T., 2021, An Introduction to Imprecise Markov Chains, Optimization under Uncertainty with Applications to Aerospace Engineering, PP. 141-179. Https://Doi.Org/ 10.1007/978-3-030-60166-9_5
Kukunuri, A.N.J., Murugan, D. & Singh, D., 2020, Variance Based Fusion Of Vci And Tci For Efficient Classification Of Agriculture Drought Using Modis Data, Geocarto International. Https://Doi.Org/ 10.1080/10106049.2020.1837256.
Kulshreshtha, S.N., 1998, A Global Outlook for Water Resources to the Year 2025, Water Resources Management, 12(3), PP. 167-184. Https://Doi.Org/10.1023/A:1007957229865
Mckee, T.B., Doesken, N.J. & Kleist, J., 1993, The Relationship of Drought Frequency and Duration to Time Scales, Eighth Conference on Applied Climatology, PP. 17-22.
Mishra, A., Alnahit, A. & Campbell, B., 2021, Impact of Land Uses, Drought, Flood, Wildfire, and Cascading Events on Water Quality and Microbial Communities: A Review and Analysis, Journal of Hydrology, 596, 125707. Https://Doi.Org/ 10.1016/J.Jhydrol.2020.125707.
Mishra, A.K. & Singh, V.P., 2010, A Review of Drought Concepts, Journal of Hydrology, 391(1-2), PP. 202-216. Https://Doi.Org/ 10.1016/J.Jhydrol.2010.07.012.
Mohamadi, S., Sammen, S.S., Panahi, F., Ehteram, M., Kisi, O., Mosavi, A.,… et al., 2020, Zoning Map for Drought Prediction Using Integrated Machine Learning Models With a Nomadic People Optimization Algorithm, Natural Hazards, 104(1), PP. 537-579. Https://Doi.Org/ 10.1007/S11069-020-04180-9/Figures/15.
Mokarram, M., Pourghasemi, H.R., Hu, M. & Zhang, H., 2021, Determining and Forecasting Drought Susceptibility in Southwestern Iran Using Multi-Criteria Decision-Making (Mcdm) Coupled with Ca-Markov Model, Science of the Total Environment, 781, P. 146703.
   Https://Doi.Org/10.1016/J.Scitotenv.2021.146703
Mokarram, M., Pourghasemi, H.R., Huang, K. & Zhang, H., 2022, Investigation Of Water Quality And Its Spatial Distribution In The Kor River Basin, Fars Province, Iran, Environmental Research, 204(Pt C) P. 112294. Https://Doi.Org/10.1016/J.Envres. 2021.112294.
Oliver, M.A. & Webster, R., 2007, Kriging: A Method of Interpolation for Geographical Information Systems, International Journal of Geographical Information Systems, 4(3), PP. 313-332. Https://Doi.Org/10.1080/ 02693799008941549.
Phan, V.H., Dinh, V.T. & Su, Z., 2020, Trends in Long-Term Drought Changes in the Mekong River Delta of Vietnam, Remote Sensing, 12(18), P. 2974. Https://Doi.Org/ 10.3390/Rs12182974.
Price, J.C., 1990, U Sing Spatial Context in Satellite Data to Infer Regional Scale Evapotranspiration, Ieee Transactions on Geoscience and Remote Sensing, 28(5), PP. 940-948. Https://Doi.Org/10.1109/36.58983.
Quiring, S.M. & Ganesh, S., 2010, Evaluating the Utility of the Vegetation Condition Index (Vci) for Monitoring Meteorological Drought in Texas, Agricultural and Forest Meteorology, 150(3), PP. 330-339. Https://Doi.Org/10.1016/J.Agrformet.2009.11.015.
Raheli, B., Aalami, M.T., El-Shafie, A., Ghorbani, M.A. & Deo, R.C., 2017, Uncertainty Assessment of the Multilayer Perceptron (Mlp) Neural Network Model with Implementation of the Novel Hybrid Mlp-Ffa Method for Prediction of Biochemical Oxygen Demand and Dissolved Oxygen: A Case Study of Langat River, Environmental Earth Sciences, 76(14), PP. 1-16. Https://Doi.Org/ 10.1007/S12665-017-6842-Z/Tables/8.
Roerink, G.J., Menenti, M. & Verhoef, W., 2000, Reconstructing Cloudfree NDVI Composites Using Fourier Analysis of Time Series, International Journal of Remote Sensing, 21(9), PP. 1911-1917. Https://Doi.Org/10.1080/014311600209814.
Saber, A., James, D.E. & Hannoun, I.A., 2020, Effects of Lake Water Level Fluctuation Due to Drought and Extreme Winter Precipitation on Mixing and Water Quality of an Alpine Lake, Case Study: Lake Arrowhead, California, Science of the Total Environment, 714, P. 136762. Https://Doi.Org/10.1016/J.Scitotenv.2020.136762.
Sandholt, I., Rasmussen, K. & Andersen, J., 2002, A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status, Remote Sensing of Environment, 79(2-3), PP. 213-224. Https://Doi.Org/10.1016/S0034-4257(01)00274-7.
Spadoni, G.L., Cavalli, A., Congedo, L. & Munafò, M., 2020, Analysis of Normalized Difference Vegetation Index (NDVI) Multi-Temporal Series for the Production of Forest Cartography, Remote Sensing Applications: Society and Environment, 20, P. 100419. Https://Doi.Org/10.1016/J.Rsase. 2020.100419.
Tomaz, A., Palma, P., Fialho, S., Lima, A., Alvarenga, P., Potes, M. & Salgado, R., 2020, Spatial and Temporal Dynamics of Irrigation Water Quality under Drought Conditions in a Large Reservoir in Southern Portugal, Environmental Monitoring and Assessment, 192(2), PP. 1-17. Https://Doi.Org/10.1007/S10661-019-8048-1/Figures/5.
Tran, Q.K., Jassby, D. & Schwabe, K.A., 2017, The Implications of Drought and Water Conservation on the Reuse of Municipal Wastewater: Recognizing Impacts and Identifying Mitigation Possibilities, Water Research, 124, PP. 472-481. Https://Doi.Org/ 10.1016/J.Watres.2017.07.069.
Vicente-Serrano, S.M., López-Moreno, J.I., Drumond, A., Gimeno, L., Nieto, R., Morán-Tejeda, E.,… et al., 2011, Effects of Warming Processes on Droughts and Water Resources in the Nw Iberian Peninsula (1930-2006), Climate Research, 48(2-3), PP. 203-212. Https://Doi.Org/ 10.3354/Cr01002.
Xie, F. & Fan, H., 2021, Deriving Drought Indices from Modis Vegetation Indices (NDVI/EVI) and Land Surface Temperature (Lst): Is Data Reconstruction Necessary?, International Journal of Applied Earth Observation and Geoinformation, 101, P. 102352. Https://Doi.Org/10.1016/J.Jag. 2021.102352.
 
نشریه سنجش از دور و GIS ایران
دوره 14، شماره 2 - شماره پیاپی 54
تیر 1401
صفحه 75-90
فایل ها
هم رسانی
ارجاع به این مقاله
آمار