Soil saturated hydraulic conductivity is considered one of the physical soil properties that is very important in modeling of water movement and environmental studies. This study aimed to compare the performance of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) in neural networks for estimation of the soil saturated hydraulic conductivity. For this, the data of 27 drilled cased borehole permeameter with three kinds of geometry water flow through the soils and the soil texture properties were used as the input parameters for models. The effectiveness of neural networks to estimate the soil saturated hydraulic conductivity were calculated and compared based on mean squared error (MSE), root mean squared error (RMSE) and coefficient determination (R2). According to the above indicators, for all three types of drilled cased borehole permeameter surveyed in this study, the results show MLP neural networks had better performance than RBF neural networks in estimation of the soil saturated hydraulic conductivity and for wells with the horizontal, vertical and horizontal-vertical flow, which the amount of coefficient determination were respectively for all of them 0.94, 0.97 and 0.85.

The DSSAT4.7-CERES model was employed to simulate plant-water nexus conditions in the future of Mazandaran province in Iran, using ensemble outputs of various GCMs and emission scenarios with LARS-WG 5.5 in the time period 2010-2100. The results showed during the 21 st century, maize water requirement is expected to be reduced by 3.3-14.1%. Under climate change scenarios, both negative and positive changes in crop yield are projected, between −37.4 and 36.1%, which consequently results in a 5.1-27.2% reduction in water use efficiency (WUE) in the future periods. Deficit irrigation (DI) with 25% reduction in irrigation water depth (DI 75 ) lead to a moderate reduction of 4.3-5.5% in WUE, but WUE was highly reduced under DI 55 . While early planting may reduce WUEs by 0.4-17%, late planting almost resulted in improved WUE, especially under DI 75 . Less frequent irrigation significantly reduces actual evapotranspiration, which consequently resulted in improved WUE by 0.57-42.47%. In conclusion, the integrated assessment reveals that DI 75 , with an irrigation interval of 5 days, together with a 20 days delay in cropping date of maize in Mazandaran province, may be considered as an effective adaptation solution, when considering both food and water security.

This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.