1. Adamala S, Raghuwanshi NS, Mishra A, Tiwari MK (2014a) Evapotranspiration modeling using second-order neural networks. J Hydrol Eng 19(6):1131–1140. doi: 10.1061/(ASCE)HE.1943-5584.0000887
2. Adamala S, Raghuwanshi NS, Mishra A, Tiwari MK (2014b) Development of generalized higher-order synaptic neural based ETo models for different agroecological regions in India. J Irrig Drain Eng. doi: 10.1061/(ASCE)IR.1943-4774.0000784
3. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper no. 56 Rome Italy
4. Chakra NC, Song K-Y, Gupta MM, Saraf DN (2013) An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs). J Pet Sci Eng. doi: 10.1016/j.petrol
5. Chen JL, Li GS, Xiao BB, Wen ZF, Lv MQ, Chen CD, Jiang Y, Wang XX, Wu SJ (2015) Assessing the transferability of support vector machine model for estimation of global solar radiation from air temperature. Energy Convers Manag 89(1):318–329