Data-Driven Techniques for Monthly Pan Evaporation Modeling in Iraq

Authors: Jazuli Abdullahi1 & Ala Tahsin2
1Department of Civil Engineering, Faculty of Civil and Environmental Engineering, Near East University, Nicosia, Cyprus
2Department of Civil Engineering, Faculty of Engineering, Tishk International University, Iraq

Abstract: Evaporation plays significant roles in agricultural production, climate change and water resources management. Hence, its accurate prediction is of paramount importance. This study aimed at investigating the potentials of artificial neural network (ANN), support vector regression (SVR) and classical multiple linear regression (MLR) models for monthly pan evaporation modeling in Erbil and Salahaddin stations of Iraq. Data including maximum, minimum, and mean temperatures, wind speed, relative humidity, and vapor pressure were used as inputs for 5 different input combinations to achieve the study objective. For performance evaluation of the applied models, root mean square error (RMSE) and determination coefficient (DC) were employed. In addition, Taylor diagrams were plotted to compare the performance of the models. The results showed that models with 6 inputs provided the best performance for Salahaddin station, but 5 inputs model led to better accuracy for MLR model in Erbil station. ANN provided superior performance with DC = 0.9527 and RMSE = 0.0660 for Erbil station while for Salahaddin station, SVR performed better with DC and RMSE of 0.8487 and 0.0753 in the validation phase. The general study results demonstrated that all the 3 applied models could be employed for successful pan evaporation modeling in the study stations, but for better accuracy, ANN is preferable.

Keywords: Artificial Neural Network, Support Vector Regression, Erbil, Data, Salahaddin

Download the PDF Document

doi: 10.23918/eajse.v6i1p104


Abdullahi, J., & Elkiran, G. (2017). Prediction of the future impact of climate change on reference evapotranspiration in Cyprus using artificial neural network. Procedia computer science, 120, 276-283.

Abdullahi, J., Elkiran, G., & Nourani, V. (2017). Application of Artificial Neural Network to predict reference evapotranspiration in Famagusta, North Cyprus. In 11th International Scientific Conference on Production Engineering Development and Modernization of Production (pp. 549-554).

Azorin-Molina, C., Vicente-Serrano, S. M., Sanchez-Lorenzo, A., McVicar, T. R., Morán-Tejeda, E., Revuelto, J., & Tomas-Burguera, M. (2015). Atmospheric evaporative demand observations estimate and driving factors in Spain (1961–2011). Journal of Hydrology, 523, 262-277.

Bewoor, M. L., Londhe, S. N., Kamble, A., Solanki, N., Nimbalkar, P., & Singh, N. (2016, March). Estimation of pan-evaporation using spatiotemporal data mining approach. In National Conference NCPCI (p. 19).

Cortes, C., & Vapnik, V. (1995). Support-vector networks Machine learning (pp. 237–297), Vol. 20.

Elkiran, G., Nourani, V., Abba, S. I., & Abdullahi, J. (2018). Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global Journal of Environmental Science and Management, 4(4), 439-450.

Ghorbani, M. A., Deo, R. C., Yaseen, Z. M., Kashani, M. H., & Mohammadi, B. (2018). Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theoretical and Applied Climatology, 133(3-4), 1119-1131.

Goyal, M. K., Bharti, B., Quilty, J., Adamowski, J., & Pandey, A. (2014). Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Systems with Applications, 41(11), 5267-5276.

Haghiabi, A. H., Azamathulla, H. M., & Parsaie, A. (2017). Prediction of head loss on cascade weir using ANN and SVM. ISH Journal of Hydraulic Engineering, 23(1), 102-110.

Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural etworks, 2(5), 359-366.

Kim, S., Shiri, J., Singh, V. P., Kisi, O., & Landeras, G. (2015). Predicting daily pan evaporation by soft computing models with limited climatic data. Hydrological Sciences Journal, 60(6), 1120-1136.

Kisi, O. (2015). Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 528, 312-320.

Legates, D. R., & McCabe Jr, G. J. (1999). Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water resources research, 35(1), 233-241.

Mehr, A. D., Nourani, V., Khosrowshahi, V. K., & Ghorbani, M. A. (2019). A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology, 16(1), 335-346.

Nourani, V., Alami, M. T., & Aminfar, M. H. (2009). A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22(3), 466-472.

Nourani, V., Sharghi, E., & Aminfar, M. H. (2012). Integrated ANN model for earthfill dam’s seepage analysis: Sattarkhan Dam in Iran. Artif. Intell. Research, 1(2), 22-37.

Nourani, V., Elkiran, G., & Abdullahi, J. (2019a). Multi-station artificial intelligence-based ensemble modeling of reference evapotranspiration using pan evaporation measurements. Journal of Hydrology, 577, 123958.

Nourani, V., Elkiran, G., Abdullahi, J., & Tahsin, A. (2019b). Multi-region modeling of daily global solar radiation with artificial intelligence ensemble. Natural Resources Research, 1-22.

Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K. W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177-187.

Rahimikhoob, A. (2009). Estimating daily pan evaporation using artificial neural network in a semi-arid environment. Theoretical and Applied Climatology, 98(1-2), 101-105.

Rasul, A., Balzter, H., & Smith, C. (2015). Spatial variation of the daytime Surface Urban Cool Island during the dry season in Erbil, Iraqi Kurdistan, from Landsat 8. Urban Climate, 14, 176-186.

Sarlak, N., & Agha, O. M. M. (2018). Spatial and temporal variations of aridity indices in Iraq. Theoretical and Applied Climatology, 133(1-2), 89-99.

Sharghi, E., Nourani, V., & Behfar, N. (2018). Earthfill dam seepage analysis using ensemble artificial intelligence-based modeling. Journal of Hydroinformatics, 20(5), 1071-1084.

Shiri, J., Marti, P., & Singh, V. P. (2014). Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning. Hydrological Processes, 28(3), 1215-1225.

Shirsath, P. B., & Singh, A. K. (2010). A comparative study of daily pan evaporation estimation using ANN, regression and climate-based models. Water Resources Management, 24(8), 1571-1581.

Valipour, M., & Eslamian, S. (2014). Analysis of potential evapotranspiration using 11 modified temperature-based models. International Journal of Hydrology Science and Technology, 4(3), 192-207.

Wang, W. C., Xu, D. M., Chau, K. W., & Chen, S. (2013). Improved annual rainfall-runoff forecasting using PSO–SVM model based on EEMD. Journal of Hydroinformatics, 15(4), 1377-1390.

Wang, L., Niu, Z., Kisi, O., Li, C. A., &Yu, D. (2017). Pan evaporation modeling using four different heuristic approaches. Computers and Electronics in Agriculture, 140, 203-213.

Yang, H., & Yang, D. (2012). Climatic factors influencing changing pan evaporation across China from 1961 to 2001. Journal of Hydrology, 414, 184-193.

Yaseen, Z. M., Ebtehaj, I., Kim, S., Sanikhani, H., Asadi, H., Ghareb, M. I., … & Shahid, S. (2019). Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water, 11(3), 502.

Zhang, Q., Qi, T., Li, J., Singh, V. P., & Wang, Z. (2015). Spatiotemporal variations of pan evaporation in China during 1960–2005: changing patterns and causes. International Journal of Climatology, 35(6), 903-912.