Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation

Estimation of evaporation is of indispensable significance for management and development of water resources. This study aims to identify the suitability of hybridizing the Cuckoo optimization algorithm (COA) with two well-known approaches of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for prediction of daily pan evaporation. For this aim, two hybrid models of ANN–COA and ANFIS–COA are developed and their performances are compared with single ANN and ANFIS. As case study, the daily climate parameters including the average air temperature (Tavg), sunshine hours (S), relative humidity (Rh), wind speed (W) and pan evaporation (E) measured and collected for three Iranian stations of Zabol, Iranshahr and Shiraz have been utilized. The used data sets are divided into three parts so that 60, 20 and 20 % of the data are applied for training, testing and prediction phases, respectively. The achieved results prove that the models’ performances are variable among cities. It is found that combining the COA with ANN and ANFIS techniques does not enhance the precision of the developed ANN and ANFIS models noticeably in all considered stations. In fact, the results demonstrate that hybridizing the COA with ANN and ANFIS cannot be a viable option for estimation of daily evaporation. Overall, the study results indicate that further accuracy can generally be achieved by the ANN model; consequently, the ANN model can be sufficiently used in the prediction of daily evaporation

Controls on the deep thermal field: implications from 3-D numerical simulations for the geothermal research site Groß Schönebeck

The deep thermal field in sedimentary basins can be affected by convection, conduction or both resulting from the structural inventory, physical properties of geological layers and physical processes taking place therein. For geothermal energy extraction, the controlling factors of the deep thermal field need to be understood to delineate favorable drill sites and exploitation compartments. We use geologically based 3-D finite element simulations to figure out the geologic controls on the thermal field of the geothermal research site Gro Schonebeck located in the E part of the North German Basin. Its target reservoir consists of Permian Rotliegend clastics that compose the lower part of a succession of Late Carboniferous to Cenozoic sediments, subdivided into several aquifers and aquicludes. The sedimentary succession includes a layer of mobilized Upper Permian Zechstein salt which plays a special role for the thermal field due to its high thermal conductivity. Furthermore, the salt is impermeable and due to its rheology decouples the fault systems in the suprasalt units from subsalt layers. Conductive and coupled fluid and heat transport simulations are carried out to assess the relative impact of different heat transfer mechanisms on the temperature distribution. The measured temperatures in 7 wells are used for model validation and show a better fit with models considering fluid and heat transport than with a purely conductive model. Our results suggest that advective and convective heat transport are important heat transfer processes in the suprasalt sediments. In contrast, thermal conduction mainly controls the subsalt layers. With a third simulation, we investigate the influence of a major permeable and of three impermeable faults dissecting the subsalt target reservoir and compare the results to the coupled model where no faults are integrated. The permeable fault may have a local, strong impact on the thermal, pressure and velocity fields whereas the impermeable faults only cause deviations of the pressure field