[1]. Abdulkadir, S. J., Yong, S-P. (2015). Scaled UKF–NARX hybrid model for multi-step-ahead forecasting of chaotic time series data. Soft Computing, 19, 3479–3496.
[2]. Allen, R. G., Pereira, L. S., Raes, D., Smith, M., (1998). Crop evapotranspiration. FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization, Rome.
[3]. Arabeyyat, O., Shatnawi, N., Matouq, M, (2018). Nonlinear multivariate rainfall prediction in Jordan using NARX-ANN model with GIS techniques. Jordan Journal of Civil Engineering, 12(3), 359-368.
[4]. Asadi Zarch, M.A., Sivakumar, B., Malekinezhad, H., Sharma, A., (2017). Future aridity under conditions of global climate change. Journal of Hydrology, 554, 451-469.
[5]. Asadi Zarch, M. A., Sivakumar, B., Sharma, A., (2015). Droughts in a warming climate: a global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI). Journal of Hydrology, 526, 183-195.
[6]. Bari Abarghouei, H., Hosseini, S. Z., (2016). Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates. Arabian Journal of Geosciences, 9(15), 663. DOI: 10.1007/s12517-016-2679-0.
[7]. Bari Abarghouei, H. B., Kousari, M. R., Asadi Zarch, M. A., (2013). Prediction of drought in dry lands through feedforward artificial neural network abilities. Arabian Journal of Geosciences, 6 (5), 1417-1433.
[8]. Boussaada, Z., Curea, O., Remaci, A., Camblong, H., Bellaaj, N. M. (2018). A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation. Energies, 11, 620, doi:10.3390/en11030620.
[9]. Byakatonda, J., Parida, B. P., Kenabatho, P. K. (2018). Relating the dynamics of climatological and hydrological droughts in semiarid Botswana. Physics and Chemistry of the Earth, 105, 12-24.
[10]. Chang, F.J., Chang, L.C., Huang, C.W., Kao, I.F. (2016). Prediction of monthly regional groundwater levels through hybrid soft-computing techniques. Journal of Hydrology, 541, 965-976
[11]. Chen, S., Billings, S., Grant, P. (1990). Non-linear system identification using neural networks. International Journal of Control, 51, 1191–1214.
[12]. Ding, R., Kang, S., Zhang, Y., Hao, X., Tong, L., Li, S., (2015). A dynamic surface conductance to predict crop water use from partial to full canopy cover. Agricultural Water Management, 150, 1–8.
[13]. Guzman, S. M., Paz, J. O., Tagert, M. L. M. (2017). The Use of NARX Neural Networks to Forecast Daily Groundwater Levels. Water Resources Management, 31 (5), 1591-1603.
[14]. Han, X., Liu, W., Lin, W., (2015). Spatiotemporal analysis of potential evapotranspiration in the Changwu tableland from 1957 to 2012. Meteorological Applications, 22, 586–591.
[15]. Horne, B.G., Giles, C.L. (1994). An experimental comparison of recurrent neural networks, in: NIPS, 697–704.
[16]. Huo, F., Poo, A.N. (2013). Nonlinear autoregressive network with exogenous inputs based contour error reduction in CNC machines. International Journal of Machine Tools and Manufacture, 67, 45-52.
[17]. Jabloun, M., Sahli, A. (2008). Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data application to Tunisia. Agricultural Water Management, 95, 707–715.
[18]. Jamil, M., Zeeshan, M.A. (2018). Comparative analysis of ANN and chaotic approach-based wind speed prediction in India. Neural Computing and Applications. Article in Press. DOI: 10.1007/s00521-018-3513-2
[19]. Martí, P., González-Altozano, P., López-Urrea, R., Mancha, L.A., Shiri, J., (2015). Modeling reference evapotranspiration with calculated targets. Assessment and implications. Agricultural Water Management, 149, 81–90.
[20]. Ruslan, F.A., Samad, A.M., Zain, Z.M., Adnan, R. (2014). Flood water level modeling and prediction using NARX neural network: Case study at Kelang river. Proceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014, art. no. 6805748, pp. 204-207.
[21]. Salehi, M., Montazeri-GH, A., Nasiri, M., (2013). Modeling of turbojet fuel control unit using NARX-neural network. Mechanical Engineering Journal, 13(7), 1-9. (in Farsi)
[22].Wang, W., Zou, S., Luo, Z., Zhang, W., Chen, D., Kong, J. (2014). Prediction of the reference evapotranspiration using a chaotic approach. The Scientific World Journal. 13 pages.
[23]. Wunsch, A., Liesch, T., Broda, S. (2018). Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). Journal of Hydrology, Article in Press. DOI: 10.1016/j.jhydrol.2018.01.045