Estimation of evapotranspiration (ET) is necessary in water resources management, farm irrigation scheduling, and environmental assessment. Hence, in practical hydrology, it is often necessary to reliably and consistently estimate evapotranspiration. In this study, two artificial intelligence (AI) techniques, including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used to compute garlic crop water requirements. Various architectures and input combinations of the models were compared for modeling garlic crop evapotranspiration. A case study in a semiarid region located in Hamedan Province in Iran was conducted with lysimeter measurements and weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation during 2008–2009. Both ANN and ANFIS models produced reasonable results. The ANN, with 6-6-1 architecture, presented a superior ability to estimate garlic crop evapotransp...

Many end-users for the stormwater harvesting systems are reluctant in implementing the system due to uncertainties of the potential returns for their investment for such system. A common practice of presenting potential benefit of a certain investment is through calculation of payback period using net annual benefit from the system. Traditional practice of doing such payback period analysis for rainwater tanks was considering individual building/roof, system volume, and specific investment cost. It is not feasible to conduct such analysis for each and every rainwater harvesting system installed in different buildings. To overcome this tedious practice, this study presents development of a generalised equation for the estimation of payback period for rainwater tanks based on roof area, initial cost, and rate of return. Based on an earlier study, several payback periods were calculated for different roof sizes, initial costs, and rate of return. It was found that all these variables can be correlated and embedded into a base equation of power function. Final developed equation results were compared with the payback periods calculated through traditional practice considering net annual savings and net present value of cumulative savings. It is found that the developed equation can estimate payback periods with very good accuracies; for all the selected internal rates of return correlation values ranging from 0.99 to 1.0 were achieved. Corresponding coefficient of determinations varied from 0.988 to 0.993. Furthermore, it is found that for a fixed roof area and rate of return, the payback period is having a power relationship (having an exponent less than 1.0) with the initial cost.

نقش پدیده انسو و خشکسالی بر دبی ایستگاه‌های هیدرومتری دو استان همدان و زنجان طی دوره آماری 32 ساله) مورد بررسی قرار گرفت. بدین منظور از شاخص خشکسالی بارش استاندارد شده (SPI) و 9 شاخص انسو شامل SOI, PNA, MEI, NINO3, NINO4, NINO1+2, NINO3.4, TNI و SST از سال 1981 تا 2012 میلادی مورد ارزیابی قرار گرفت. مقایسه اثر شاخص‌های مختلف انسو بر تغییرات دبی نشان داد که بیشترین همبستگی به شاخص نوسان ‌جنوبی (SOI) تعلّق داشت. همچنین اثر انسو بر تغییرات دبی ایستگاه‌های هیدرومتری به ‌مراتب بیش از اثر خشکسالی بود. به‌طوری که میانگین درصد ایستگاه‌های دارای همبستگی معنی‌دار با شاخص SOI در استان‌های همدان و زنجان به ‌ترتیب 5/36 و 42/42 درصد بود. درحالی که متوسط درصد ایستگاه‌های دارای همبستگی معنی‌دار با شاخص SPI برای استان‌های همدان و زنجان به‌ترتیب 5/24 و 83/24 بدست آمد. ضمن آن‌که اثر فاز ال‌نینو نیز افزایش دبی ایستگاه‌های هیدرومتری را به دنبال داشت.