mohammmad javad rezaei; mohammad reza rezaei
Abstract
AbstractIn dry and semi-arid areas, water is the most factor of limiter in agriculture. In these areas, due to the lack of surface flows, major pressures enter on groundwater. Groundwater resources in the studied area (Dashte-Abbas plain) also suffered a severe drop in surface water due to unplanned ...
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AbstractIn dry and semi-arid areas, water is the most factor of limiter in agriculture. In these areas, due to the lack of surface flows, major pressures enter on groundwater. Groundwater resources in the studied area (Dashte-Abbas plain) also suffered a severe drop in surface water due to unplanned use. In this study, we compared four different models of evolutionary neural network, a multi-layered perceptron neural network with Genetic Algorithm (ANN-GA), a multilayered perceptron neural network with particle swarm optimization (ANN-PSO), a multilevel perceptron neural network with Imperialism competitive algorithm (ANN-ICA) and multi-layered perceptron neural network with ant colony optimization (ANN-ACOR) for estimating groundwater level according to groundwater inflow, effective penetration of rainfall, effective penetration of surface flow and flood, effective penetration of return water Agriculture, underground outflow, withdrawal from aquifer for agriculture, evaporation from groundwater level and past groundwater data Were used. groundwater level comparisons are the combination of inputs has been prepared using Auto-correlation analysis, partial Auto-correlation and cross-correlation for each model. Optimal models are obtained by changing the control parameters. The best results are obtained from the input models (GWLt-1, GWLt-2, Qint, Qpt-1, Qrt-1, Qit-1, Qoutt-1, Qwt-1, and Qet-1). The accuracy of the mean squared error in the test phase for ANN-PSO, ANN-ICA, ANN-ACOR models was 1.2208, 0.9456 and 1.7720, respectively, and for the ANN-GA model, it was 0.8739. The mean relative error of ANN-GA model is 3.6% and its determined coefficient is 0.9388. According to the results, the ANN-GA model showed better performance than the other three models for estimating groundwater level.
Jafar Rezaei; Heydar Seydzadeh; Alireza Shadmani
Abstract
Flood control, artifcial feeding of ground water aquifers and effort to optimize the productivity of natural resources are component the most results that have gotten from implementation of spreading project in spreading statious of country. One of the most important goals of spreading is the creation ...
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Flood control, artifcial feeding of ground water aquifers and effort to optimize the productivity of natural resources are component the most results that have gotten from implementation of spreading project in spreading statious of country. One of the most important goals of spreading is the creation of positive changes in economical variables of area including agricultural and rangeland productionsand their development. In other words, each of spreading projects implemented in different stations have specific economical effects in desired area. The goal of this project is determination of economical effects of floodwater spreading project on Dehloran aquifer through monitoring and surveying its behavior and recording relevant data steadily. For this purpose, profit ability and economical justification of the project have been determined through economical indicator evaluation of projects. Results demonstrated that irrigated lands of downstream villages have been inceased 3.5 times in 2010 compared to the year1995. The added value of the injected floodwater was estimated to be 14.6 billionRial. The added value of rangeland products was about 1353.96 billion Rial. The added value of wood production was estimated about 13.38 billion Rial. The added value of agricultual products was about 657.05 billion Rial. NPV, ROR and B/C of the project were 1398.61 billion Rial, 0.38 and 2.87, respectively that show economical justification of the project.
