زراعت و فناوری زعفران

زراعت و فناوری زعفران

ارزیابی مدل هیبریدی شبکه عصبی کواتی ANN-COA برای پیش بینی نیاز آبی زعفران با استفاده از پارامترهای اقلیمی محدود

نوع مقاله : مقاله علمی پژوهشی

نویسندگان
1 استاد گروه علوم و مهندسی آب دانشکده کشاورزی دانشگاه بیرجند
2 استادیار گروه مهندسی برق و کامپیوتر دانشگاه تربت حیدریه
3 دانشجوی دکتری منابع آب، گروه علوم و مهندسی آب، دانشگاه بیرجند
چکیده
تخمین دقیق نیاز آبی زعفران برای مدیریت پایدار منابع آب در مناطق کاشت این محصول ضروری است. در این پژوهش، بهینه‌سازی مدل شبکه عصبی مصنوعی (ANN) برای تخمین نیاز آبی زعفران با استفاده از الگوریتم بهینه‌ساز هیبریدی کواتی (COA) بررسی شد. عملکرد مدل ANN-COA با مدل‌های ANN، ANN-GA، ANN-PSO، ANN-MFO، رگرسیون مرتبه دوم (QR)، رگرسیون درختی (TR) و رگرسیون الگویی (Pattern) مقایسه شد. داده‌های ورودی شامل دما (حداقل، حداکثر، متوسط)، سرعت باد، رطوبت نسبی، تابش خالص و روز از سال بود. نتایج نشان داد که در شرایط استفاده از کلیه پارامترهای اقلیمی، مدل ANN-COA با ضریب تعیین 0.995=R2 و خطای میانگین مربعات 0.0001=MSE برای ایستگاه مشهد و 0.973=R2 و 0.0005=MSE برای ایستگاه بیرجند، دقت قابل قبولی در تخمین نیاز آبی زعفران دارد. همچنین در شرایط استفاده از پارامترهای اقلیمی محدود، مدل ANN-COA با ترکیب دمای حداکثر و سرعت باد به همراه روز از سال، بهترین عملکرد را در تخمین نیاز آبی زعفران داشت. بر اساس یافته‌های این پژوهش، مدل‌های شبکه عصبی هیبریدی برای تخمین نیاز آبی زعفران در شرایط استفاده از حداقل پارامترهای اقلیمی، در مقایسه با سایر مدل‌های داده‌کاوی، از دقت بالاتری برخوردار می‌باشند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Evaluation of the Hybrid Artificial Neural Network-Coati Optimization Algorithm (ANN-COA) Model for Predicting Saffron Water Demand Using Limited Climatic Parameters

نویسندگان English

Abbas Khashei Siuki 1
Ali Maroosi 2
Moein Tosan 3
1 University of birjand
2 Assistant Professor, University of Torbat Heydarieh, Torbat Heydarieh, Iran
3 Ph.D. Candidate of Water Resources, University of Birjand.
چکیده English

Accurate estimation of saffron water demand is essential for sustainable water resource management in saffron-growing regions. This study examines the optimization of the Artificial Neural Network (ANN) model for predicting saffron water demand using the hybrid Coati Optimization Algorithm (COA). The performance of the ANN-COA model was compared with ANN, ANN-GA, ANN-PSO, ANN-MFO, Quadratic Regression (QR), Tree Regression (TR), and Pattern Regression models. Input data included temperature (minimum, maximum, average), wind speed, relative humidity, net radiation, and day of the year. The results showed that under conditions using all climatic parameters, the ANN-COA model achieved an R² of 0.995 and a Mean Squared Error (MSE) of 0.0001 for the Mashhad station, and an R² of 0.973 and MSE of 0.0005 for the Birjand station, indicating acceptable accuracy in predicting saffron water demand. Additionally, under conditions with limited climatic parameters, the ANN-COA model, using maximum temperature, wind speed, and day of the year, exhibited the best performance in predicting saffron water demand. Based on the findings of this research, hybrid neural network models show superior accuracy in estimating saffron water demand with minimal climatic parameters compared to other data mining models.

کلیدواژه‌ها English

Optimization
Saffron
Machine learning
Water demand
Water resourses management
 
Abou Houran, M., Bukhari, S. M. S., Zafar, M. H., Mansoor, M., & Chen, W. (2023). COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. Applied Energy, 349, 121638. https://doi.org/10.1016/j.apenergy.2023.121638
Abrishami, N., Sepaskhah, A. R., & Shahrokhnia, M. H. (2019). Estimating wheat and maize daily evapotranspiration using artificial neural network. Theoretical & Applied Climatology, 135, 945-958. https://doi.org/10.1007/s00704-018-2418-4
Abualigah, L., Almotairi, K. H., & Elaziz, M. A. (2023). Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: Comparative analysis, open challenges and new trends. Applied Intelligence, 53 (10), 11654-11704. https://doi.org/10.1007/s10489-022-04064-4
Abyaneh, H. Z., Nia, A. M., Varkeshi, M. B., Marofi, S., & Kisi, O. (2011). Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration. Journal of Irrigation & Drainage Engineering, 137 (5), 280-286. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000298
Ahmadee, M., Khashei Siuki, A., & Sayyari, M. (2016). Comparison of efficiency of different equations to estimate the water requirement in saffron (Crocus sativus L.) (Case study: Birjand plain, Iran). Journal of Agroecology, 8 (4), 505-520.  (In Persian with English abstract). https://doi.org/10.22067/JAG.V8I4.40517
Ahmed, F. (2018). An IoT-big data based machine learning technique for forecasting water requirement in irrigation field. Research and Practical Issues of Enterprise Information Systems: 11th IFIP WG 8.9 Working Conference, CONFENIS 2017, Shanghai, China, October 18-20, 2017, Revised Selected Papers 11, https://doi.org/10.1007/978-3-319-94845-4_7
Akbari, A., Ziaei, A. N., Naghedifar, S. M., Moghaddam, P. R., & Sharafkhane, M. G. (2024). Simulation of saffron growth using AquaCrop model with high-resolution measured data. Scientia Horticulturae, 324, 112569. https://doi.org/10.1016/j.scienta.2023.112569
Al-Zahrani, M. A., & Abo-Monasar, A. (2015). Urban residential water demand prediction based on artificial neural networks and time series models. Water Resources Management, 29, 3651-3662. https://doi.org/10.1007/s11269-015-1021-z
Alam, M. M., Akter, M. Y., Islam, A. R. M. T., Mallick, J., Kabir, Z., Chu, R., Arabameri, A., Pal, S. C., Al Masud, M. A., & Costache, R. (2024). A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models. Journal of Environmental Management, 351, 119714. https://doi.org/10.1016/j.jenvman.2023.119714
Alhijawi, B., & Awajan, A. (2024). Genetic algorithms: Theory, genetic operators, solutions, and applications. Evolutionary Intelligence, 17 (3), 1245-1256. https://doi.org/10.1007/s12065-023-00822-6
Aliakbari, P., Salari, A., & KhasheiSiuki, A. (2018). Determine of the actual and potential evapotranspiration and appropriate model for determining water requirement of saffron (Case study: Torbat Heydarieh). Iranian Journal of Ecohydrology, 5 (3), 1051-1061. (In Persian with English abstract). https://doi.org/10.22059/IJE.2018.252321.830
Anele, A. O., Hamam, Y., Abu-Mahfouz, A. M., & Todini, E. (2017). Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction. Water, 9 (11), 887. https://doi.org/10.3390/w9110887
Azari, P., Sobhanardakani, S., Cheraghi, M., Lorestani, B., & Goodarzi, A. (2024). A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran. Environmental Science & Pollution Research, 1-14. https://doi.org/10.1007/s11356-024-32919-5
Bian, J., Hu, X., Shi, L., Min, L., Zhang, Y., Shen, Y., Zhao, F., Zha, Y., Lian, X., & Huang, J. (2024). Improving the evapotranspiration estimation by considering the effect of flux footprint climatology. Journal of Hydrology, 631, 130769. https://doi.org/10.1016/j.jhydrol.2024.130769
Braga-Neto, U. (2020). Fundamentals of Pattern Recognition and Machine Learning. Springer.
Carrizosa, E., Molero-Río, C., & Romero Morales, D. (2021). Mathematical optimization in classification and regression trees. Top, 29 (1), 5-33. https://doi.org/10.1007/s11750-021-00594-1
Dehghani, M., Montazeri, Z., Trojovská, E., & Trojovský, P. (2023). Coati optimization algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011. https://doi.org/10.1016/j.knosys.2022.110011
Elbeltagi, A., Deng, J., Wang, K., & Hong, Y. (2020). Crop water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt. Agricultural Water Management, 235, 106080. https://doi.org/10.1016/j.agwat.2020.106080
Elshara, R., Hançerlioğullari, A., Rahebi, J., & Lopez-Guede, J. M. (2024). PV cells and modules parameter estimation using coati optimization algorithm. Energies, 17 (7), 1716. https://doi.org/10.3390/en17071716
Ewaid, S. H., Abed, S. A., & Al-Ansari, N. (2019). Crop water requirements and irrigation schedules for some major crops in Southern Iraq. Water, 11 (4), 756. https://doi.org/10.3390/w11040756
Fallahi, H.-r., & Mahmoodi, S. (2018). Influence of organic and chemical fertilization on growth and flowering of saffron under two irrigation regimes. Saffron Agronomy & Technology, 6 (2), 147-166(In Persian with English abstract). https://doi.org/10.22048/jsat.2017.71511.1207
Fallahi, H. R., Zamani, G., Aghhavani Shajari, M., Samadzadeh, A., Branca, F., & Mehrabani, M. (2017). Saffron flower and stigma yield changes in response to application of different levels of super absorbent polymer. Journal of Medicinal Plants & By-Product, 6 (2), 145-151. https://doi.org/10.22092/JMPB.2017.113537
Fang, S. L., Lin, Y. S., Chang, S. C., Chang, Y. L., Tsai, B. Y., & Kuo, B. J. (2024). Using artificial intelligence algorithms to estimate and short-term forecast the daily reference evapotranspiration with limited meteorological variables. Agriculture, 14 (4), 510. https://doi.org/10.3390/agriculture14040510
Feizi, H., & Tosan, M. (2016). Saffron yield variability by climatic factors in the northeast of Iran. In V International Symposium on Saffron Biology and Technology: Advances in Biology, Technologies, Uses and Market 1184 (pp. 109-114). https://doi.org/10.17660/ActaHortic.2017.1184.15
Gandomkar, A., Ezzatian, V., Behyar, M., Ghayoor, H., & Rajabi, Z. (2015). Estimation evapotranspiration by Penman Monteith method and its water require in Isfahan province. Geographical Research, 30 (116), 239-252.
Ghavamsaeidi Noghabi, S., Khashei-Siuki, A., Hammami, H., Shahidi, A., & Yaghoobzadeh, M. (2020). Determination of evapotranspiration and crop coefficient of saffron (Crocus sativus L.) by lysimetric method in the dry-desert climate of Birjand. Journal of Saffron Research, 8 (1), 161-172. (In Persian with English abstract). https://doi.org/10.22077/jsr.2019.2515.1101
Goyal, P., Kumar, S., & Sharda, R. (2023). A review of the Artificial Intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives. Computers & Electronics in Agriculture, 209, 107836. https://doi.org/10.1016/j.compag.2023.107836
Guo, X., Sun, X., & Ma, J. (2011). Prediction of daily crop reference evapotranspiration (ET0) values through a least-squares support vector machine model. Hydrology Research, 42 (4), 268-274. https://doi.org/10.1016/j.aej.2020.03.020
Han, S., Cao, Y., Wu, X., Xu, J., Nie, Z., & Qiu, Y. (2024). New horizons for the study of saffron (Crocus sativus L.) and its active ingredients in the management of neurological and psychiatric disorders: A systematic review of clinical evidence and mechanisms. Phytotherapy Research, 38 (5), 2276-2302 https://doi.org/10.1002/ptr.8110
Hegde, M. A., Naik, M. S., Chaitra, S., Madhavi, M., & Ravichandra, A. (2021). Prediction and analysis of water requirement in automated irrigation system using artificial neural network (ANN) and lora technology. 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). https://doi.org/10.1109/DISCOVER52564.2021.9663706
Houssein, E. H., Hammad, A., Emam, M. M., & Ali, A. A. (2024). An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition. Computers in Biology & Medicine, 173, 108329. https://doi.org/10.1016/j.compbiomed.2024.108329
Hu, P., Tong, J., Wang, J., Yang, Y., & de Oliveira Turci, L. (2019). A hybrid model based on CNN and Bi-LSTM for urban water demand prediction. 2019 IEEE Congress on Evolutionary Computation (CEC), https://doi.org/10.1109/CEC.2019.8790060
Jafarzadeh, A., Khashei-Siuki, A., & Shahidi, A. (2015). Modeling of climate change effects on saffron water requirement in south Khorasan province by GIS. Journal of Saffron Research, 3 (2), 163-174. (In Persian with English abstract).  https://doi.org/https://doi.org/10.22077/jsr.2015.292
Jafarzadeh, A., Khashei Siuki, A., & Shahidi, A. (2023). Applicability of ensemble modeling techniques in water requirement simulations. Saffron Agronomy & Technology, 11 (2), 163-182. (In Persian with English abstract). https://doi.org/10.22048/jsat.2023.394323.1486
Ji, B. X., Liu, H. H., Cheng, P., Ren, X. Y., Pi, H. D., & Li, L. L. (2024). Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm. Journal of Energy Storage, 91, 112093. https://doi.org/10.1016/j.est.2024.112093
KhasheiSiuki, A., Shahidi, A., Dastorani, M., Fallahi, H. R., & Shirzadi, F. (2023). Yield and quality of sesame (Sesamum indicum L.) improve by water preservative materials under normal and deficit irrigation in Birjand. Agrotechniques in Industrial Crops, 3 (3), 121-132. https://doi.org/10.22126/ATIC.2023.9167.1098
Kolahi, M., Davary, K., & Omranian Khorasani, H. (2024). Integrated approach to water resource management in Mashhad plain, Iran: actor analysis, cognitive mapping, and roadmap development. Scientific Reports, 14 (1), 162. https://doi.org/10.1038/s41598-023-50697-x
Koocheki, A., Ebrahimian, E., & Seyyedi, S. M. (2016). How irrigation rounds and mother corm size control saffron yield, quality, daughter corms behavior and phosphorus uptake. Scientia Horticulturae, 213, 132-143. https://doi.org/10.1016/j.scienta.2016.10.028
Koocheki, A., Fallahi, H. R., & Jami-Al-Ahmadi, M. (2020). Saffron water requirements. In Saffron (pp. 67-92). Elsevier. https://doi.org/10.1016/B978-0-12-818638-1.00006-X
Koocheki, A., Moghaddam, P. R., Aghhavani-Shajari, M., & Fallahi, H. R. (2019). Corm weight or number per unit of land: Which one is more effective when planting corm, based on the age of the field from which corms were selected? Industrial Crops & Products, 131, 78-84. https://doi.org/10.1016/j.indcrop.2019.01.026
Kumar, M., Raghuwanshi, N., & Singh, R. (2011). Artificial neural networks approach in evapotranspiration modeling: A review. Irrigation Science, 29, 11-25. https://doi.org/10.1007/s00271-010-0230-8
Lei, W., Wang, G., Wan, B., Min, Y., Wu, J., & Li, B. (2024). High voltage shunt reactor acoustic signal denoising based on the combination of VMD parameters optimized by coati optimization algorithm and wavelet threshold. Measurement, 224, 113854. https://doi.org/10.1016/j.measurement.2023.113854
Mahdian Moghadam, N., & Tosan, M. (1394/2015). Study of Socio-Economic Effects of Saffron Cultivation in Torbat Heydarieh and Neyshabur. International Conference on Management, Culture and Economic Development, (p. 1-7), Mashhad, Fayyad Research Institute. [In Persian]
Maleki, M., Ebrahimzade, H., Gholami, M., & Niknam, V. (2011). The effect of drought stress and exogenous abscisic acid on growth, protein content and antioxidative enzyme activity in saffron (Crocus sativus L.). African Journal of Biotechnology, 10 (45), 9068-9075. https://doi.org/10.5897/AJB10.676
Matsui, H. (2020). Quadratic regression for functional response models. Econometrics & Statistics, 13, 125-136. https://doi.org/10.1016/j.ecosta.2018.12.003
Mehmeti, A., Candido, V., Canaj, K., Castronuovo, D., Perniola, M., D’Antonio, P., & Cardone, L. (2024). Energy, environmental, and economic sustainability of saffron cultivation: Insights from the first European (Italian) Case study. Sustainability, 16 (3), 1179. https://doi.org/10.3390/su16031179
Moayedi, H., Foong, L. K., & Le, B. N. (2024). Three intelligent computational models to predict the high-performance concrete mixture. Neural Computing & Applications, 36 (7), 3479-3498. https://doi.org/10.1007/s00521-023-09233-1
Moshizi, Z. G. N., Bazrafshan, O., Etedali, H. R., Esmaeilpour, Y., & Collins, B. (2023). Application of inclusive multiple model for the prediction of saffron water footprint. Agricultural Water Management, 277, 108125. https://doi.org/10.1016/j.agwat.2022.108125
Niroomandfad, F., Khashei Siuki, A., Hashemi, S. R., & Ghorbani, K. (2023). Investigating the water footprint of saffron production in Birjand Plain under climate change conditions. Saffron Agronomy & Technology, 11 (3), 301-320. (In Persian with English abstract). https://doi.org/10.22048/jsat.2023.413847.1506
Peng, Y., Xiao, Y., Fu, Z., Dong, Y., Zheng, Y., Yan, H., & Li, X. (2019). Precision irrigation perspectives on the sustainable water-saving of field crop production in China: Water demand prediction and irrigation scheme optimization. Journal of Cleaner Production, 230, 365-377. https://doi.org/10.1016/j.jclepro.2019.04.347
Pereira, L., Paredes, P., & Jovanovic, N. (2020). Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach. Agricultural Water Management, 241, 106357. https://doi.org/10.1016/j.agwat.2020.106357
Ramos, T. B., Darouich, H., & Pereira, L. S. (2024). Mulching effects on soil evaporation, crop evapotranspiration and crop coefficients: a review aimed at improved irrigation management. Irrigation Science, 1-15. https://doi.org/10.1007/s00271-024-00924-8
Razmavaran, M. H., Sepaskhah, A. R., & Ahmadi, S. H. (2024a). Revisiting reference evapotranspiration calculation under regional advection and its effect on single and dual crop coefficients: An empirical approach for quinoa crop. Meteorological Applications, 31 (2), e2189. https://doi.org/10.1002/met.2189
Razmavaran, M. H., Sepaskhah, A. R., & Ahmadi, S. H. (2024b). Water footprint and production of rain-fed saffron under different planting methods with ridge plastic mulch and pre-flowering irrigation in a semi-arid region. Agricultural Water Management, 291, 108632. https://doi.org/10.1016/j.agwat.2023.108632
Rezvani Moghaddam, P., Karbasi, A., Tosan, M., Gharari, F., Feizi, H., & Mohtashami, T. (2016). Saffron Agronomy and Technology (Book of Abstracts: 2013-2016). Saffron Agronomy & Technology, 4 (SUPPLEMENT), 1-78. [In Persian]. https://doi.org/10.22048/jsat.2016.39250
Saini, A. K., Bhatnagar, R., & Srivastava, D. K. (2024). SADCNN-ORBM: a hybrid deep learning model based citrus disease detection and classification. International Journal of Electrical & Computer Engineering (IJECE), 14 (2), 2191-2201. http://doi.org/10.11591/ijece.v14i2.pp2191-2201
Sammen, S. S., Ehteram, M., Sheikh Khozani, Z., & Sidek, L. M. (2023). Binary coati optimization algorithm-Multi-Kernel least square support vector machine-extreme learning machine model (BCOA-MKLSSVM-ELM): A New hybrid machine learning model for predicting reservoir water level. Water, 15 (8), 1593. https://doi.org/10.3390/w15081593
Sepaskhah, A. R., & KAMGAR, H. A. (2009). Saffron irrigation regime. International Journal of Plant Production, 3 (1), 1-16. https://doi.org/10.22069/IJPP.2012.627
Shamsabadi, V., Far, A. M., Tohidi, R., & Mirzaei, S. (2016). Evaluation of water consumption productivity of saffron in Iran (Case study: the province of Khorasan Razavi). International Journal of Agriculture & Biosciences, 5 (3), 102-104. https://doi.org/10.22077/jwhr.2023.6663.1101
Suriyan, K., & Nagarajan, R. (2024). Particle swarm optimization in biomedical technologies: innovations, challenges, and opportunities. Emerging Technologies for Health Literacy & Medical Practice, 220-238. https://doi.org/10.4018/979-8-3693-1214-8.ch011
Tian, Z. (2020). A combined prediction approach based on wavelet transform for crop water requirement. Water Supply, 20 (3), 1016-1034. https://doi.org/10.2166/ws.2020.024
Tosan, M., Alizadeh, A., Ansari, H., & Rezvani Moghaddam, P. (2015). Evaluation of yield and identifying potential regions for Saffron (Crocus sativus L.) cultivation in Khorasan Razavi province according to temperature parameters. Saffron agronomy and technology, 3(1), 1-12. (In Persian with English abstract). https://doi.org/10.22048/jsat.2014.9605
Tosan, M., Gharib, M. R., Attar, N. F., & Maroosi, A. (2025). Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management. Computer Modeling in Engineering & Sciences (CMES), 142(2). https://doi.org/10.32604/cmes.2025.058595
Tosan, M., Khashei Siuki, A., Sangari, M., & Rezvani Moghaddam, P. (2024). Analysis of the global research trend of saffron (Crocus sativus L.) between 2000-2023. Saffron Agronomy and Technology, 12(2), 115-138. (In Persian with English abstract). https://doi.org/10.22048/jsat.2024.443037.1524
Tosan, M., & Maroosi, A. (2024). Investigating the performance of artificial rabbit optimization hybrid algorithm (ANN-ARO) in forecasting reference evapotranspiration with limited climatic parameters. Iranian Journal of Rainwater Catchment Systems, 12 (1), 47-66. (In Persian with English abstract). https://dor.isc.ac/dor/20.1001.1.24235970.1403. 12.1.3.6
Veysi, S., Nouri, M., & Jabbari, A. (2024). Reference evapotranspiration estimation using reanalysis and WaPOR products in dryland Croplands. Heliyon, 10, e26531. https://doi.org/10.1016/j.heliyon.2024.e26531
Wang, C., Lin, H., Hu, H., Yang, M., & Ma, L. (2024). A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction. Energy, 130684. https://doi.org/10.1016/j.energy.2024.130684
Yarami, N., & Sepaskhah, A. (2016). Effect of irrigation water salinity, manure application and planting method on soil ions variation and ions uptake by saffron (Crocus sativus L.). International Journal of Plant Production, 10 (2). https://doi.org/10.22069/IJPP.2016.2788
Yarami, N., & Sepaskhah, A. R. (2015). Saffron response to irrigation water salinity, cow manure and planting method. Agricultural Water Management, 150, 57-66. https://doi.org/10.1016/j.agwat.2014.12.004
Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., & Chau, K. W. (2019). An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387-408. https://doi.org/10.1016/j.jhydrol.2018.11.069
Yin, S., Du, H., Mao, F., Li, X., Zhou, G., Xu, C., & Sun, J. (2024). Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought. Science of the Total Environment, 913, 169439. https://doi.org/10.1016/j.scitotenv.2023.169439
Zamani, H., Nadimi-Shahraki, M. H., Mirjalili, S., Soleimanian Gharehchopogh, F., & Oliva, D. (2024). A critical review of moth-flame optimization algorithm and its variants: structural reviewing, performance evaluation, and statistical analysis. Archives of Computational Methods in Engineering, 1-49. https://doi.org/10.1007/s11831-023-10037-8
Zhang, J., Zhu, Y., & Chen, F. (2008). Forecast research of crop water requirements based on fuzzy rules. Computer and computing technologies in agriculture. Volume II: First IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture (CCTA 2007), Wuyishan, China, August 18-20, 2007 1. https://doi.org/10.1007/978-0-387-77253-0_61
Zhao, L., Qing, S., Li, H., Qiu, Z., Niu, X., Shi, Y., Chen, S., & Xing, X. (2024). Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data. International Journal of Biometeorology, 1-15. https://doi.org/10.1007/s00484-023-02608-y
Zhao, N., Chen, X., Su, Y., Jiang, Y., & Wang, X. (2024). Wind pressure field reconstruction using a variance-extended KSI method: Both deterministic and probabilistic applications. Probabilistic Engineering Mechanics, 75, 103557. https://doi.org/10.1007/s00484-023-02608-y
Zhou, Y., Huang, R., Lin, Q., Chai, Q., & Wang, W. (2024). Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty. International Journal of Electrical Power & Energy Systems, 157, 109897.  https://doi.org/10.1016/j.ijepes.2024.109897