نوع مقاله : فنی و ترویجی
نویسنده
گروه علوم و مهندسی آب، مجتمع آموزش عالی تربت‌جام، تربت‌جام، ایران
چکیده
تبخیر و تعرق یکی از مهمترین مؤلفه‌های بیلان آب و انرژی است که تخمین دقیق آن برای مدیریت منابع آب، برنامه‌ریزی آبیاری و مطالعات محیطزیستی ضروری است. در سال‌های اخیر، استفاده از تکنیک‌های یادگیری ماشین (ML) راهکار امیدوارکننده‌ای برای بهبود دقت پیش‌بینی تبخیر و تعرق ارائه کرده است. در این مطالعه، دو مدل شبکه عصبی مصنوعی، شامل پرسپترون چند لایه (MLP) و شبکه با پایه شعاعی (RBF)، برای تخمین تبخیر و تعرق گیاه مرجع در ایستگاه‌های مشهد، سبزوار و تربت جام در استان خراسان رضوی به‌کار گرفته شدند. داده‌های هواشناسی شامل دما، رطوبت نسبی، سرعت باد و ساعات آفتابی از سال 1371 تا 1402 جمع‌آوری و پس از نرمال‌سازی برای آموزش مدل‌ها استفاده شدند. مقادیر تبخیر و تعرق محاسبه شده با روش فائو-پنمن-مانتیث به‌عنوان داده‌های هدف جهت ارزیابی مدل‌ها در نظر گرفته شد. نتایج نشان داد این مدل‌ها، به‌ویژه MLP، عملکرد مطلوبی در تخمین تبخیر و تعرق داشته و دقت بالاتری نسبت به روش‌های لیناکر و هارگریوز-سامانی ارائه کردند. همچنین، حذف سرعت باد در ایستگاه‌های تربت جام و مشهد موجب کاهش 5/1 درصدی و در ایستگاه سبزوار باعث کاهش 11/3 درصدی ضریب تعیین (R²) شد، درحالی‌که حذف ساعات آفتابی تأثیر ناچیزی بر دقت مدل داشت. این یافته‌ها بر پتانسیل بالای شبکه‌های عصبی مصنوعی در برآورد تبخیر و تعرق در شرایط کمبود داده تأکید داشته و می‌توانند به مدیریت بهینه منابع آب کشاورزی در منطقه مورد مطالعه کمک کنند.
کلیدواژه‌ها
موضوعات
احمدزاده قره گویز، کاوه، میرلطیفی، سید مجید، و محمدی، کوروش. (1389). مقایسه سیستم‌های هوش مصنوعی ANN) و (ANFIS در تخمین میزان تبخیر-تعرق گیاه مرجع در مناطق بسیار خشک ایران. نشریه آب و خاک، 4(24)،679-689.
doi: 10.22067/jsw.v0i0.3887
صیادی، حبیب، اولاد غفاری، ابوالفتح، فعالیان، احد، و صدرالدینی، علی اشرف. (1388). مقایسه عملکرد شبکه‌های عصبی RBF و MLP در برآورد تبخیر و تعرق گیاه مرجع. دانش آب و خاک، 19(1)، 1-12.
doi: 10.22067/jsw.v0i0.3887
صیادی، حبیب، اولاد غفاری، ابوالفتح، فعالیان، احد، و صدرالدینی، علی اشرف. (1388). مقایسه عملکرد شبکه‌های عصبی RBF و MLP در برآورد تبخیر و تعرق گیاه مرجع. دانش آب و خاک، 19(1)، 1-12.
Abrishami, N., Sepaskhah, A. R., & Shahrokhnia, M. H. (2019). Estimating wheat and maize daily evapotranspiration using artificial neural network. Theoretical and Applied Climatology, 135(3), 945–958.
https://doi.org/10.1007/s00704-018-2418-4
Adnan, R. M., Malik, A., Kumar, A., Parmar, K. S., & Kisi, O. (2019). Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arabian Journal of Geosciences, 12(19), 606.
https://doi.org/10.1007/s12517-019-4781-6
Allen, R. G., Clemmens, A. J., Burt, C. M., Solomon, K., & O’Halloran, T. (2005). Prediction Accuracy for Projectwide Evapotranspiration Using Crop Coefficients and Reference Evapotranspiration. Journal of Irrigation and Drainage Engineering, 131(1), 24-36.
doi: 10.1061/(ASCE)0733-9437(2005)131:1(24)
Amani, S., & Shafizadeh-Moghadam, H. (2023). A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data. Agricultural Water Management, 284, 108324.
https://doi.org/10.1016/j. agwat. 2023.108324
Amiri, M., & Pourghasemi, H. R. (2019). Comparing Different Methods of Potential Evapotranspiration and Studying Temporal and Spatial Changes in the Mahalou Watershed using GIS. Jwmr, 10(19), 22–35.
https://doi.org/10.29252/jwmr.10.19.22
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., & Kustas, W. P. (2007). A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research: Atmospheres, 112(D11).
https://doi.org/10.1029/2006JD007507
Antonopoulos, V. Z., & Antonopoulos, A. V. (2017). Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Computers and Electronics in Agriculture, 132, 86–96.
https://doi.org/10.1016/j.compag. 2016.11.011
Bishop, C. M. (1996). Neural Networks for Pattern Recognition. Oxford, Oxford University Press. Oxford, England. https://doi.org/10.1093/oso/9780198538493.001.0001
Bruton, J. M., McClendon, R. W., & Hoogenboom, G. (2000). Estimating daily pan evaporation with artificial neural networks. Transactions of the ASAE, 43(2), 491–496. https://doi.org/10.13031/2013.2730
Dehbozorgi, F., & Sepaskhah, A. R. (2012). Comparison of artificial neural networks and prediction models for reference evapotranspiration estimation in a semi-arid region. Archives of Agronomy and Soil Science, 58(5), 477–497. https://doi.org/10.1080/03650340.2010.530255
Diamantopoulou, M., Georgiou, P. E., & Papamichail, D. M. (2011). Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data. Global Nest Journal, 13(1), 18–27. https://doi.org/10.30955/gnj. 000758
Feng, Y., Cui, N., Zhao, L., Hu, X., & Gong, D. (2016). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology, 536, 376–383. https://doi.org/10.1016/j. jhydrol. 2016.02.053
Ghahreman, N., & Sameti, M. (2014). Comparison of M5 Model Tree and Artificial Neural Network for Estimating Potential Evapotranspiration in Semi-arid Climates. Desert, 19(1), 75-81. doi: 10.22059/jdesert.2014.51056
Hargreaves, G. H., Samani, Y. A., George H. Hargreaves, & Zohrab A. Samani. (1985). Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1(2), 96–99. https://doi.org/10.13031/2013.26773
Jang, J. -C., Sohn, E. -H., Park, K. -H., & Lee, S. (2021). Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula. Hydrology, 8(3).
https://doi.org/10.3390/hydrology8030129
Khoshhal, J., & Mokarram, M. (2012). Model for prediction of evapotranspiration using MLP neural network. International. Journal of Environmental Sciences, 3(3), 1000–1009.
Kisi, O. (2008). The potential of different ANN techniques in evapotranspiration modelling. Hydrological Processes, 22(14), 2449–2460.
https://doi.org/10.1002/hyp. 6837
Kumar, M., Raghuwanshi, N. S., & Singh, R. (2011). Artificial neural networks approach in evapotranspiration modeling: A review. Irrigation Science, 29(1), 11–25.
https://doi.org/10.1007/s00271-010-0230-8
Kumar, M., Raghuwanshi, N, S., Singh, R., Wallender, W, W., & Pruitt, W, O. (2002). Estimating Evapotranspiration using Artificial Neural Network. Journal of Irrigation and Drainage Engineering, 128(4), 224–233.
https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)
Laaboudi, A., Mouhouche, B., & Draoui, B. (2012). Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. International Journal of Biometeorology, 56(5), 831–841.
https://doi.org/10.1007/s00484-011-0485-7
Landeras, G., Ortiz-Barredo, A., & López, J. J. (2008). Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management, 95(5), 553–565.
https://doi.org/10.1016/j. agwat. 2007.12.011
Lang, D., Zheng, J., Shi, J., Liao, F., Ma, X., Wang, W., Chen, X., & Zhang, M. (2017). A comparative study of potential evapotranspiration estimation by eight methods with FAO Penman–Monteith method in southwestern China. Water (Switzerland), 9(10), 734.
https://doi.org/10.3390/w9100734
Linacre, E. T. (1977). A simple formula for estimating evaporation rates in various climates, using temperature data alone. Agricultural Meteorology, 18(6), 409-424.
https://doi.org/10.1016/0002-1571(77)90007-3
Liu, Y., Zhang, S., Zhang, J., Tang, L., & Bai, Y. (2021). Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors. Remote Sensing, 13(19). https://doi.org/10.3390/rs13193838
Maroufpoor, S., Bozorg-Haddad, O., & Maroufpoor, E. (2020). Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm. Journal of Hydrology, 588, 125060. https://doi.org/10.1016/j. jhydrol. 2020.125060
Marquina-Araujo, J. J., Cotrina-Teatino, M. A., Mamani-Quispe, J. N., Soto-Juscamayta, L. M., Ccatamayo-Barrios, J. H., Ortiz-Quintanilla, S. M., & Cruz-Galvez, J. A. (2024). Application of Multilayer Perceptron Neural Network in Geological Modeling of Categorical Variables: A Case Study in Peru. Mathematical Modelling of Engineering Problems, 11(6), 1463–1472.
https://doi.org/10.18280/mmep. 110607
Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin, S., & Han, D. (2009). Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1), 88–97.
https://doi.org/10.1016/j. advwatres. 2008.10.005
Mohawesh, O. E. (2013). Artificial neural network for estimating monthly reference evapotransiration under arid and semi arid environments. Archives of Agronomy and Soil Science, 59(1), 105–117.
https://doi.org/10.1080/03650340.2011.603126
Odhiambo, L, O., Yoder, R, E., Yoder, D, C., & Hines, J, W. (2001). Optimization of fuzzy evapotranspiration model through neural training with input–output examples. Transactions of the ASAE, 44(6), 1625.
https://doi.org/10.13031/2013.7049
Orth, R., & Destouni, G. (2018). Drought reduces blue-water fluxes more strongly than green-water fluxes in Europe. Nature Communications, 9(1), 3602.
https://doi.org/10.1038/s41467-018-06013-7
Rahimi Khoob, A. (2008). Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrigation Science, 26(3), 253–259.
https://doi.org/10.1007/s00271-007-0090-z
ROSENBLATT, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.
https://doi.org/10.1037/h0042519
Shirmohammadi-Aliakbarkhani, Z., Saberali, S. F., & Kouhi, M. (2020). Evaluation of Different Methods of Calculating the Potential Evapotranspiration at the Annual Timescale in the Northeast of Iran. Jwmr, 11(22), 199–209.
https://doi.org/10.52547/jwmr.11.22.199
Slavisa, T., Branimir, T., & Miomir, S. (2003). Forecasting of Reference Evapotranspiration by Artificial Neural Networks. Journal of Irrigation and Drainage Engineering, 129(6), 454–457.
https://doi.org/10.1061/(ASCE)0733-9437(2003)129:6(454)
Teuling, A. J., Hirschi, M., Ohmura, A., Wild, M., Reichstein, M., Ciais, P., Buchmann, N., Ammann, C., Montagnani, L., Richardson, A. D., Wohlfahrt, G., & Seneviratne, S. I. (2009). A regional perspective on trends in continental evaporation. Geophysical Research Letters, 36, L02404.
https://doi.org/10.1029/2008GL036584
Traore, S., Wang, Y. -M., & Kerh, T. (2010). Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agricultural Water Management, 97(5), 707–714.
https://doi.org/10.1016/j.agwat.2010.01.002
Wang, Y. -M., Traore, S., Kerh, T., & Leu, J. -M. (2011). Modelling reference evapotranspiration using feed forward backpropagation algorithm in arid regions of Africa. Irrigation and Drainage, 60(3), 404–417.
https://doi.org/10.1002/ird.589
Zanetti, S., Sousa E, F., Oliveira V, P., Almeida F, T., & Bernardo, S. (2007). Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data. Journal of Irrigation and Drainage Engineering, 133(2), 83–89.
https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(8
https://doi.org/10.1007/s00704-018-2418-4
Adnan, R. M., Malik, A., Kumar, A., Parmar, K. S., & Kisi, O. (2019). Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arabian Journal of Geosciences, 12(19), 606.
https://doi.org/10.1007/s12517-019-4781-6
Allen, R. G., Clemmens, A. J., Burt, C. M., Solomon, K., & O’Halloran, T. (2005). Prediction Accuracy for Projectwide Evapotranspiration Using Crop Coefficients and Reference Evapotranspiration. Journal of Irrigation and Drainage Engineering, 131(1), 24-36.
doi: 10.1061/(ASCE)0733-9437(2005)131:1(24)
Amani, S., & Shafizadeh-Moghadam, H. (2023). A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data. Agricultural Water Management, 284, 108324.
https://doi.org/10.1016/j. agwat. 2023.108324
Amiri, M., & Pourghasemi, H. R. (2019). Comparing Different Methods of Potential Evapotranspiration and Studying Temporal and Spatial Changes in the Mahalou Watershed using GIS. Jwmr, 10(19), 22–35.
https://doi.org/10.29252/jwmr.10.19.22
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., & Kustas, W. P. (2007). A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research: Atmospheres, 112(D11).
https://doi.org/10.1029/2006JD007507
Antonopoulos, V. Z., & Antonopoulos, A. V. (2017). Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Computers and Electronics in Agriculture, 132, 86–96.
https://doi.org/10.1016/j.compag. 2016.11.011
Bishop, C. M. (1996). Neural Networks for Pattern Recognition. Oxford, Oxford University Press. Oxford, England. https://doi.org/10.1093/oso/9780198538493.001.0001
Bruton, J. M., McClendon, R. W., & Hoogenboom, G. (2000). Estimating daily pan evaporation with artificial neural networks. Transactions of the ASAE, 43(2), 491–496. https://doi.org/10.13031/2013.2730
Dehbozorgi, F., & Sepaskhah, A. R. (2012). Comparison of artificial neural networks and prediction models for reference evapotranspiration estimation in a semi-arid region. Archives of Agronomy and Soil Science, 58(5), 477–497. https://doi.org/10.1080/03650340.2010.530255
Diamantopoulou, M., Georgiou, P. E., & Papamichail, D. M. (2011). Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data. Global Nest Journal, 13(1), 18–27. https://doi.org/10.30955/gnj. 000758
Feng, Y., Cui, N., Zhao, L., Hu, X., & Gong, D. (2016). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology, 536, 376–383. https://doi.org/10.1016/j. jhydrol. 2016.02.053
Ghahreman, N., & Sameti, M. (2014). Comparison of M5 Model Tree and Artificial Neural Network for Estimating Potential Evapotranspiration in Semi-arid Climates. Desert, 19(1), 75-81. doi: 10.22059/jdesert.2014.51056
Hargreaves, G. H., Samani, Y. A., George H. Hargreaves, & Zohrab A. Samani. (1985). Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1(2), 96–99. https://doi.org/10.13031/2013.26773
Jang, J. -C., Sohn, E. -H., Park, K. -H., & Lee, S. (2021). Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula. Hydrology, 8(3).
https://doi.org/10.3390/hydrology8030129
Khoshhal, J., & Mokarram, M. (2012). Model for prediction of evapotranspiration using MLP neural network. International. Journal of Environmental Sciences, 3(3), 1000–1009.
Kisi, O. (2008). The potential of different ANN techniques in evapotranspiration modelling. Hydrological Processes, 22(14), 2449–2460.
https://doi.org/10.1002/hyp. 6837
Kumar, M., Raghuwanshi, N. S., & Singh, R. (2011). Artificial neural networks approach in evapotranspiration modeling: A review. Irrigation Science, 29(1), 11–25.
https://doi.org/10.1007/s00271-010-0230-8
Kumar, M., Raghuwanshi, N, S., Singh, R., Wallender, W, W., & Pruitt, W, O. (2002). Estimating Evapotranspiration using Artificial Neural Network. Journal of Irrigation and Drainage Engineering, 128(4), 224–233.
https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)
Laaboudi, A., Mouhouche, B., & Draoui, B. (2012). Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. International Journal of Biometeorology, 56(5), 831–841.
https://doi.org/10.1007/s00484-011-0485-7
Landeras, G., Ortiz-Barredo, A., & López, J. J. (2008). Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management, 95(5), 553–565.
https://doi.org/10.1016/j. agwat. 2007.12.011
Lang, D., Zheng, J., Shi, J., Liao, F., Ma, X., Wang, W., Chen, X., & Zhang, M. (2017). A comparative study of potential evapotranspiration estimation by eight methods with FAO Penman–Monteith method in southwestern China. Water (Switzerland), 9(10), 734.
https://doi.org/10.3390/w9100734
Linacre, E. T. (1977). A simple formula for estimating evaporation rates in various climates, using temperature data alone. Agricultural Meteorology, 18(6), 409-424.
https://doi.org/10.1016/0002-1571(77)90007-3
Liu, Y., Zhang, S., Zhang, J., Tang, L., & Bai, Y. (2021). Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors. Remote Sensing, 13(19). https://doi.org/10.3390/rs13193838
Maroufpoor, S., Bozorg-Haddad, O., & Maroufpoor, E. (2020). Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm. Journal of Hydrology, 588, 125060. https://doi.org/10.1016/j. jhydrol. 2020.125060
Marquina-Araujo, J. J., Cotrina-Teatino, M. A., Mamani-Quispe, J. N., Soto-Juscamayta, L. M., Ccatamayo-Barrios, J. H., Ortiz-Quintanilla, S. M., & Cruz-Galvez, J. A. (2024). Application of Multilayer Perceptron Neural Network in Geological Modeling of Categorical Variables: A Case Study in Peru. Mathematical Modelling of Engineering Problems, 11(6), 1463–1472.
https://doi.org/10.18280/mmep. 110607
Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin, S., & Han, D. (2009). Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1), 88–97.
https://doi.org/10.1016/j. advwatres. 2008.10.005
Mohawesh, O. E. (2013). Artificial neural network for estimating monthly reference evapotransiration under arid and semi arid environments. Archives of Agronomy and Soil Science, 59(1), 105–117.
https://doi.org/10.1080/03650340.2011.603126
Odhiambo, L, O., Yoder, R, E., Yoder, D, C., & Hines, J, W. (2001). Optimization of fuzzy evapotranspiration model through neural training with input–output examples. Transactions of the ASAE, 44(6), 1625.
https://doi.org/10.13031/2013.7049
Orth, R., & Destouni, G. (2018). Drought reduces blue-water fluxes more strongly than green-water fluxes in Europe. Nature Communications, 9(1), 3602.
https://doi.org/10.1038/s41467-018-06013-7
Rahimi Khoob, A. (2008). Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrigation Science, 26(3), 253–259.
https://doi.org/10.1007/s00271-007-0090-z
ROSENBLATT, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.
https://doi.org/10.1037/h0042519
Shirmohammadi-Aliakbarkhani, Z., Saberali, S. F., & Kouhi, M. (2020). Evaluation of Different Methods of Calculating the Potential Evapotranspiration at the Annual Timescale in the Northeast of Iran. Jwmr, 11(22), 199–209.
https://doi.org/10.52547/jwmr.11.22.199
Slavisa, T., Branimir, T., & Miomir, S. (2003). Forecasting of Reference Evapotranspiration by Artificial Neural Networks. Journal of Irrigation and Drainage Engineering, 129(6), 454–457.
https://doi.org/10.1061/(ASCE)0733-9437(2003)129:6(454)
Teuling, A. J., Hirschi, M., Ohmura, A., Wild, M., Reichstein, M., Ciais, P., Buchmann, N., Ammann, C., Montagnani, L., Richardson, A. D., Wohlfahrt, G., & Seneviratne, S. I. (2009). A regional perspective on trends in continental evaporation. Geophysical Research Letters, 36, L02404.
https://doi.org/10.1029/2008GL036584
Traore, S., Wang, Y. -M., & Kerh, T. (2010). Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agricultural Water Management, 97(5), 707–714.
https://doi.org/10.1016/j.agwat.2010.01.002
Wang, Y. -M., Traore, S., Kerh, T., & Leu, J. -M. (2011). Modelling reference evapotranspiration using feed forward backpropagation algorithm in arid regions of Africa. Irrigation and Drainage, 60(3), 404–417.
https://doi.org/10.1002/ird.589
Zanetti, S., Sousa E, F., Oliveira V, P., Almeida F, T., & Bernardo, S. (2007). Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data. Journal of Irrigation and Drainage Engineering, 133(2), 83–89.
https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(8
)
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