7. Summary and conclusions

the FAO-56 PM were developed considering Tmax, Tmin, RHmax, RHmin, Ws, and Sra as input and the FAO-56 PM ETo as target. Table 4 shows the performance statistics of ANN and MLR models for 15 locations during testing. Comparison of results obtained using MLR and ANN models indicated that the ANN models performed better than the MLR models for all locations except for Bangalore. This is confirmed

Time series plots of ANN and FAO-56 PM ETo for (a) Parbhani, (b) Hissar, (c) Faizabad, and (d) Dapoli locations.

) and high values of R<sup>2</sup> for ANN models as

from the low values of RMSE (mm day<sup>1</sup>

Advanced Evapotranspiration Methods and Applications

compared to the MLR models.

Figure 8.

40

Evapotranspiration is an important and one of the most difficult components of the hydrologic cycle to quantify accurately. Prior to designing any irrigation system, the information on crop water requirements or crop evapotranspiration is needed, which can be calculated using reference evapotranspiration. There exist direct measurement methods (lysimeters) and indirect estimation procedures (physical and empirical based) for modeling ETo. Direct methods have the limitations of arduous, cost-effective, and lack of skilled manpower to collect accurate measurements. The difficulty in estimating ETo with the indirect physically based methods is due to the limitations of unavailability of all necessary climate data, whereas the application of empirical methods are limited due to unsuitability of these methods for all climatic conditions and need of local calibration. ANNs are efficient in modeling complex processes without formulating any mathematical relationships related to the physical process. This study was undertaken to develop ANN models corresponding to FAO-56 PM conventional ETo method for 15 individual stations in India.

The potential of ANN models corresponding to the FAO-56 PM method was evaluated for 15 locations. The ANN models were developed considering six inputs (Tmax, Tmin, RHmax, RHmin, Ws, and Sra) and the FAO-56 PM ETo as target. The optimum number of hidden neurons was finalized with a trial of 1–15 hidden nodes. The ANN models gave lower RMSE values at i +1(i = number of inputs) hidden nodes for estimating ETo. Comparison results of MLR and ANN models indicated that the ANN models performed better for all locations. However, on an average the over- and under-estimations of ETo (<3% which is negligible) estimated by using MLR models was less as compared to ANN models. In brief, based on the above discussion on ETo modeling, the following specific conclusions are drawn:
