The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neurofuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells.Also based on comparisons, it is found that the NF computing techniques have better performance than the ANN models in this case.
Qualitative and quantitative restrictions on water resources have given rise to large water stress on land and plants. The recognition of such stresses can be of help in crop management. Due to the large impact on yield, water stress plays an important role in planning proper irrigation, timing, and amount of water needed by plants. Crop Water Stress Index (CWSI) is used for monitoring and quantifying water stress as well as for irrigation scheduling. This study was conducted for the purpose of determining the Maize (SC-701) irrigation Scheduling, use of leaf temperature in the north of Isfahan, Iran, in the crop year 2013, with five irrigation areas, where the amount of Total Available Water (TAW) was 35, 65, 75, 85, 100% respectively, in four replications. Results revealed that in return of TAW from 35 to 100%, Leaf and air temperature difference (Tl-Ta) reached 4 °C. CWSI rose about three times. CWSI in the day before the irrigation in treatment T1 and T5 was about 0.12 and 0.46, respectively. The results revealed that non-stress equation for corn in the T3 (75%TAW), was 7105 .and stress equation was completely fixed and is equal to 2.3. CWSI index is based on irrigation planning and it was 0.24. Examining yield results revealed that irrigation scheduling in this area should be done by treatment at 75% TAW.
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