|تعداد مشاهده مقاله||106,332,828|
|تعداد دریافت فایل اصل مقاله||83,216,628|
پایش و مدلسازی تغییرات سطحی دریاچه ارومیه با استفاده از شبکه عصبی مصنوعی
|مقاله 4، دوره 46، شماره 2، شهریور 1399، صفحه 295-317 اصل مقاله (2.05 M)|
|نوع مقاله: مقاله پژوهشی|
|شناسه دیجیتال (DOI): 10.22059/jes.2021.304189.1008026|
|علی رادمان1؛ مهدی آخوندزاده هنزائی* 2|
|1گروه فتوگرامتری و سنجش از دور، دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران|
|2استادیار گرایش سنجش از دور، گروه مهندسی نقشهبرداری، پردیس دانشکدههای فنی، دانشگاه تهران|
|دریاچه ارومیه یکی از بزرگترین پهنههای آبی شور در جهان است که در سالهای اخیر در شرایط بحرانی قرار داشته است. در این مطالعه، تغییرات این دریاچه و حوضه آبخیز آن بررسی گردید. سپس قابلیتهای شبکهی عصبی مصنوعی در پیش بینی تغییرات سطحی دریاچه مورد ارزیابی قرار گرفت. بدین ترتیب با استفاده از دادههای سنجنده TRMM، مدل هیدرولوژیکی GLDAS، سنجنده GRACE، سری ماهوارههای ارتفاع سنجی Jason و همچنین تصاویر MODIS به ترتیب میزان بارش، تغییرات احجام آبی سطحی و زیر سطحی (TWS)، تغییرات ارتفاعی و سطحی دریاچه ارومیه در بازه 183 ماه بین آوریل 2002 تا ژوئن 2017 محاسبه گردید. در ادامه با استفاده از دو روش مبتنی بر یادگیری ماشین MLP و LSTM و بهکارگیری پارامترهای موثر بر تغییرات سطحی دریاچه به عنوان ورودی شبکه، تغییرات سطحی دریاچه با جذر خطای مربعات ماندههای 0511/0 توسط شبکه بهینه LSTM مدلسازی شد. همچنین به منظور پیشبینی تغییرات سطحی دریاچه برای مدت زمان طولانیتر، چهار مدل برای تخمین تغییرات 3، 6، 9 و 12 ماه بعد، تشکیل شدند که در نتیجه آن، شبکه LSTM این تغییرات را برای یک سال آینده با دقتی بالا (جذر خطای مربعات ماندههای 0882/0) و توانایی مناسب در شناسایی تغییرات فصلی، تخمین زد.|
|ارتفاع سنجی؛ دریاچه ارومیه؛ شبکه عصبی؛ مدل سازی؛ GRACE|
|عنوان مقاله [English]|
|Monitoring and Modeling of Urmia Lake Area Variations Using Artificial Neural Network|
|Ali Radman1؛ Mehdi Akhoondzadeh2|
|1Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran|
|2Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran|
Due to increase of water exploitation and drought, the need for water resources has risen in past decades. Numerous regions around the world are under threat of environmental crisis, as a result of climate change. Declination in the amount of precipitation can be led to various subsequences, such as significant reduction in the level of ground and surface water, e.g., lakes. Through the development of satellite imagery systems, it is possible to monitor and evaluate changes in rainfall, groundwater level, surface water area, and level.
Numerous studies have been conducted to observe and evaluate climate change after the launch of Gravity Recovery and Climate Experiment (GRACE) satellite mission. GRACE dataset has been used widely to determine water storage variations over the world as well as Iran. This satellite data has been used for various purposes including ground and surface water monitoring. Employing this dataset beside precipitation and satellite altimetry data have been used for observing changes in watersheds and lakes in numerous studies. Modelling and predicting environmental and climate changes are always an important task. Gathering several remote sensing data and predicting them would be helpful mostly for disaster management and also decision making.
Therefore, it is possible to observe and evaluate variation in rainfall, groundwater level, surface water area, and level. In this study, Urmia Lake and its watershed changes were monitored using various satellite data such as TRMM, GLDAS, GRACE, MODIS. Moreover, machine-learning based methods were developed to predict the lake surface changes.
Materials & Methods
To monitor Urmia lake changes, several data were used to survey variation in precipitation, ground and surface water storage, lake water level, and area in 183 months from April 2002 to June 2017. Sufficient temporal resolution of the data is an essential factor in monitoring of changes through the time. Accordingly, for monitoring the overall change of the Urmia lake, we prefer a satellite data with at least monthly temporal resolution. Therefore, overall variations of the lake and its corresponding basin were modeled using these data with adequate temporal resolution.
Tropical Rainfall Measuring Mission (TRMM) is an international collaboration which aims to observe rainfall for environmental studies. TRMM data provides precipitation in various temporal and spatial resolutions. In this study, TRMM-3b43 level 3 monthly data, with 0.25 degree spatial resolution estimates rainfall in Urmia lake basin, including 83 pixels in each time step.
The GLDAS hydrological model consists of various variables (e.g., soil temperature, soil moisture, precipitation, etc.). In this study, The GLDAS data with 1 degree spatial resolution provides terrestrial water storage (TWS) by integrating soil moisture (kg m-2), snow water equivalent (kg m-2), and canopy water storage (kg m-2). Three types of monthly GLDAS model data (MOS, VIC, and NOAH) were hired for this purpose.
GRACE is a joint missions between Germany and the USA, giving information about mass changes within Earth. The level 2 (RL05) data was of GRACE was used to monitor TWSA, which was computed from spherical harmonics using methods developed by Wahr and Swanson. In addition, a 300 km Gaussian filter was applied to reduce high frequency noises.
The investigated Global Reservoirs and Lakes Monitor (G-REALM) dataset including Jason-1, Jason-2/OSTM, and Jason-3 altimeters was employed to survey Water Level (WL) variation of Urmia lake.
In order to monitor lake extent changes during the 17 years, MODIS atmospheric corrected product MOD09Q1 version 6 data, with 250 meters spatial and 8-day temporal resolution was used through Google Earth Engine. The product provides surface spectral reflectance of bands 1 and 2, which is the composite of 8 products with the absence of clouds, cloud shadow, and aerosol loading. Although, the Normalized Difference Water Index (NDWI) is a common method to separate water from land and it also had the best result on Landsat data, Normalized Difference Vegetation Index (NDVI) performs transcendent distinguishing between water and land while using MODIS data and also in the specific case of Urmia lake. Therefore, in this study, the NDVI index was chosen as an appropriate index to separate water and non-water. To determine lake area, firstly, water region was detected. Then, area of water extent was computed as lake area.
For modeling the lake's area variation, machine learning based methods were investigated. As a time-series prediction problem, a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) networks were constructed using TRMM rainfall, GLDAS, GRACE TWS, and altimeter WL as inputs (predictors) of the models, and lake's area as Target. About 80% of data was assigned to training, 10% to validation, and the same portion to test. A feedforward MLP including one hidden layer and 5 neurons and a Recurrent LSTM network with same hidden layer and 10 neurons, were obtained. In order to evaluate network's performance, Root Mean Square Error (RMSE) was used. In addition, the delay parameter of 12 months or one year was chosen for estimating future variations.
Results & Discussion
Except seasonal changes, amount of monthly rainfall during the mentioned period experienced a significant decrease from 2004 to 2008, and then it fluctuates to 2017. The changes in precipitation rate can affect other parameters considerably. As a result, water mass variation obtained from GLDAS data, falls from 2003 to 2008, and after that, similarly to rainfall variation, it fluctuates. However, TWSA computed by GRACE data, after reduction to 2008 and rise to 2010, behaved otherwise, and it went down steadily to 2017. Urmia lake WL declined during the whole period. This decrement was intensified from 2006 to 2010, after that it halted gradually to 2017 as consequence of increase in rainfall rate. Area of the lake decreased from 2004 to 2015, also it faced an extreme fall in 2008. Next, to 2017 the area increased slightly.
Due to a decade drought of Urmia lake, it was in critical circumstance. Consequently, estimating future variation of the lake is necessary. Instead of using physical models or assessing the impact of each parameter on the surface of the lake directly and indirectly, which are complicated tasks, a machine-learning based method is hired. Disregarding the exact relation between factors, this learning based method can determine and model changes. By using two of the most common ANN based methods including MLP and LSTM, variation of the lake during that period was modeled.
MLP and LSTM models reached overall RMSE (for normalized data) of 0.0586 and 0.0511, respectively, which indicates reliability of both models for predicting lake area changes, however LSTM network performed superior specially over test data (RMSE of 0.0487). In addition, to predict Urmia lake's further changes and assess LSTM model capabilities comprehensively, 4 networks were constructed to predict lake area of next 3, 6, 9, and 12 months. Accordingly, result demonstrates LSTM abilities for predicting upcoming year variation of the lake with RMSE of 0.0882 (better than prediction for 6 and 9 months).
Variation in each part of environment and climate (such as rainfall, TWS, WL and area of lakes) affects others. Therefore, it is possible to monitor and model these relations between the parameters. In this study, two ANN methods of MLP and LSTM were investigated to model Urmia lake surface area which the LSTM model performed transcendent. Moreover, LSTM method provides a model which is able to predict the lake area of next 12 months with a high accuracy.
In order to improve the network’s accuracy, it is suggested to increase the number of data and parameters, which are used as network input. It would help the network to implement the training stage with a higher capability to recognize diverse situations properly.
|Neural network, Prediction, Urmia Lake, Water Level|
Alborzi, A., A. Mirchi, H. Moftakhari, I. Mallakpour, S. Alian, A. Nazemi, E. Hassanzadeh, O. Mazdiyasni, S. Ashraf and K. Madani (2018). Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts. Environmental Research Letters 13(8): 084010.
Alesheikh, A. A., A. Ghorbanali and N. Nouri (2007). Coastline change detection using remote sensing. International Journal of Environmental Science & Technology 4(1): 61-66.
Ashrafzadeh Afshar, A., G. R. Joodaki and M. A. Sharifi (2016). Evaluation of Groundwater Resources in Iran Using GRACE Gravity Satellite Data. Journal of Geomatics Science and Technology 5(4): 73-84.
Bengio, Y., P. Simard and P. Frasconi (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks 5(2): 157-166.
Bishop, C. M. (2006). Pattern recognition and machine learning, springer.
Chen, H., W. Zhang, N. Nie and Y. Guo (2019). Long-term groundwater storage variations estimated in the Songhua River Basin by using GRACE products, land surface models, and in-situ observations. Science of the Total Environment 649: 372-387.
Cheng, M., J. C. Ries and B. D. Tapley (2011). Variations of the Earth's figure axis from satellite laser ranging and GRACE. Journal of Geophysical Research: Solid Earth 116(B1).
Dastranj, H., F. Tavakoli and A. Soltanpour (2018). Investigating the water level and volume variations of Lake Urmia using satellite images and satellite altimetry. Journal of Geographical Data (SEPEHR) 27(107): 149-163.
Delju, A., A. Ceylan, E. Piguet and M. Rebetez (2013). Observed climate variability and change in Urmia Lake Basin, Iran. Theoretical and applied climatology 111(1-2): 285-296.
Demuth, H. and M. Beale (2000). Neural network toolbox user’s guide.
Ducet, N., P.-Y. Le Traon and G. Reverdin (2000). Global high‐resolution mapping of ocean circulation from TOPEX/Poseidon and ERS‐1 and‐2. Journal of Geophysical Research: Oceans 105(C8): 19477-19498.
Faraji, Z., A. Kaviani and A. Ashrafzadeh (2017). Assessment of GRACE satellite data for estimating the groundwater level changes in Qazvin province. Ecohydrology 4(2): 463-476.
Fatolahzadeh, F., B. Voosoghi, m. Raoofian-Naeeni, M. Mohebi and R. Javadi Azar (2016). Determination of the correction due to hydrological and oceanic effects in study of the gravity variations. Journal of Geospatial Information Technology 4(2): 13-28.
Feidas, H. (2010). Validation of satellite rainfall products over Greece. Theoretical and Applied climatology 99(1-2): 193-216.
Feng, L., C. Hu, X. Chen, X. Cai, L. Tian and W. Gan (2012). Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010. Remote Sensing of Environment 121: 80-92.
Feyisa, G. L., H. Meilby, R. Fensholt and S. R. Proud (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140: 23-35.
Forootan, E., R. Rietbroek, J. Kusche, M. Sharifi, J. Awange, M. Schmidt, P. Omondi and J. Famiglietti (2014). Separation of large scale water storage patterns over Iran using GRACE, altimetry and hydrological data. Remote Sensing of Environment 140: 580-595.
Gamboa, J. C. B. (2017). Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887.
Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau and R. Moore (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202: 18-27.
Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.
Hassanzadeh, E., M. Zarghami and Y. Hassanzadeh (2012). Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resources Management 26(1): 129-145.
Hochreiter, S. and J. Schmidhuber (1997). Long short-term memory. Neural computation 9(8): 1735-1780.
Huffman, G. J., D. T. Bolvin, E. J. Nelkin, D. B. Wolff, R. F. Adler, G. Gu, Y. Hong, K. P. Bowman and E. F. Stocker (2007). The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of hydrometeorology 8(1): 38-55.
Joodaki, G. (2014). Earth mass change tracking using GRACE satellite gravity data (PhD thesis), NTNU Trondheim.
Karbassi, A., G. N. Bidhendi, A. Pejman and M. E. Bidhendi (2010). Environmental impacts of desalination on the ecology of Lake Urmia. Journal of Great Lakes Research 36(3): 419-424.
Khazaei, B., S. Khatami, S. H. Alemohammad, L. Rashidi, C. Wu, K. Madani, Z. Kalantari, G. Destouni and A. Aghakouchak (2019). Climatic or regionally induced by humans? Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy. Journal of hydrology 569: 203-217.
Long, D., Y. Shen, A. Sun, Y. Hong, L. Longuevergne, Y. Yang, B. Li and L. Chen (2014). Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data. Remote Sensing of Environment 155: 145-160.
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing 17(7): 1425-1432.
Mohebzadeh, H. (2018). Extracting AL Relationship for Urmia Lake, Iran Using MODIS NDVI/NDWI Indices. Journal of Hydrogeology & Hydrologic Engineering 7: 2.
Okay Ahi, G. and S. Jin (2019). Hydrologic Mass Changes and Their Implications in Mediterranean-Climate Turkey from GRACE Measurements. Remote Sensing 11(2): 120.
Rodell, M., P. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich and M. Bosilovich (2004). The global land data assimilation system. Bulletin of the American Meteorological Society 85(3): 381-394.
Rohli, R. V., T. Andrew Joyner, S. J. Reynolds, C. Shaw and J. R. Vázquez (2015). Globally Extended Kӧppen–Geiger climate classification and temporal shifts in terrestrial climatic types. Physical Geography 36(2): 142-157.
Rokni, K., A. Ahmad, A. Selamat and S. Hazini (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote sensing 6(5): 4173-4189.
Sun, A. Y. (2013). Predicting groundwater level changes using GRACE data. Water Resources Research 49(9): 5900-5912.
Swenson, S., D. Chambers and J. Wahr (2008). Estimating geocenter variations from a combination of GRACE and ocean model output. Journal of Geophysical Research: Solid Earth 113(B8).
Tourian, M., O. Elmi, Q. Chen, B. Devaraju, S. Roohi and N. Sneeuw (2015). A spaceborne multisensor approach to monitor the desiccation of Lake Urmia in Iran. Remote Sensing of Environment 156: 349-360.
Wahr, J., M. Molenaar and F. Bryan (1998). Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research: Solid Earth 103(B12): 30205-30229.
Williams, R. J. and D. Zipser (1989). A learning algorithm for continually running fully recurrent neural networks. Neural computation 1(2): 270-280.
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing 27(14): 3025-3033.
Zarghami, M. (2011). Effective watershed management; case study of Urmia Lake, Iran. Lake and Reservoir Management 27(1): 87-94.
Zhou, Y., S. Jin, R. Tenzer and J. Feng (2016). Water storage variations in the Poyang Lake Basin estimated from GRACE and satellite altimetry. Geodesy and Geodynamics 7(2): 108-116.
تعداد مشاهده مقاله: 515
تعداد دریافت فایل اصل مقاله: 292