Upstream Cutoff and Downstream Filters to Control of Seepage in Dams

The finite element method was used in this study to investigate cutoff walls and downstream filters to control seepage, the exit hydraulic gradient, and uplift forces for dams. Experimental data was used for validating the numerical modelling. The effective parameters are the length of filter and its distance downstream from the dam, the depth of the cutoff walls, the upstream dam head, and the thickness of alluvial foundation. The results show that by increasing filter length, the exit hydraulic gradient, uplift force, and seepage are reduced. The optimum relative length of the filter is L/H?=?0.028 which results in a decrease of about 65% in the exit hydraulic gradient, a 35% decrease in seepage and 10% reduction in the uplift force at the upstream foundation and a 60% decrease in the uplift force at the downstream foundation. Increase of cutoff wall depth reduces the exit hydraulic gradient, uplift force, and seepage. Using two cutoff walls both upstream and downstream of the dam decreases seepage, hydraulic gradient, and uplift force 132%, 450% and 11% respectively. However, using an upstream cutoff and downstream filter decreases seepage, hydraulic gradient, and uplift force by 180%, 490%, and 119% respectively. Thus, based on this study, recommendations for suitable combinations of upstream cutoff and downstream filter are provided.

     

Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.     

What labelling policy for consumer choice? The case of genetically modified food in Canada and Europe

Abstract. Faced with divergent opinions among consumers on the use of genetically modified (GM) foods, Canada has adopted a voluntary labelling approach for non‐GM foods, whereas the European Union has a mandatory labelling policy for GM foods. Interestingly, both labelling systems have resulted in very little, if any, additional consumer choice. Using an analytical model, we show that the coexistence of GM and non‐GM products at the retail level depends on the labelling policy, consumer perceptions, and the type of product. Although voluntary labelling tends to favour the use of GM products, it is more likely to provide consumer choice.     

Explaining International Differences in Genetically Modified Food Labeling Policies*

Many countries have adopted labeling policies for genetically modified (GM) food, and the regulations vary considerably across countries. We evaluate the importance of political‐economic factors implicit in the choice of GM food labeling regulations. Using an analytical model, we show that production and trade‐related interests play a prominent role in labeling decision‐making. This conclusion is validated by an empirical analysis of GM food labeling policy choices. We find that countries producing GM crops are more likely to have less stringent labeling policies. Food and feed exporters to the European Union (EU) and Japan are more likely to have adopted stricter labeling policies. Labeling regulations in Asia and Europe are similar to those of Japan and the EU. Countries with no labeling policies are less developed, with important rural sectors and are more likely to have ratified the Cartagena Protocol on Biosafety.     

Democracy and environmental quality

We develop and estimate an econometric model of the relationship between several local and global air pollutants and economic development while allowing for critical aspects of the socio-political-economic regime of a State. We obtain empirical support for our hypothesis that democracy and its associated freedoms provide the conduit through which agents can exercise their preferences for environmental quality more effectively than under an autocratic regime, thus leading to decreased concentrations or emissions of pollution. However, additional factors such as income inequality, age distribution, education, and urbanization may mitigate or exacerbate the net effect of the type of political regime on pollution, depending on the underlying societal preferences and the weights assigned to those preferences by the State.     

Conditions for Sustainable Optimal Economic Development

This paper shows that, for dynamic optimizing economies with different types of natural resource, environmental, and human‐made capital stocks, a necessary and sufficient condition for permanently sustaining an optimal utility/consumption level is the stationarity of the current‐value Hamiltonian. For economies whose development is not exogenously and directly affected by time (i.e., time‐autonomous economies), this stationarity condition generalizes Dixit et al.’s (1980 ) “zero‐net‐aggregate‐investment” rule of sustain‐ability, which in turn generalizes Solow‐Hartwick’s sustainability rule. For non‐autonomous economies, the stationarity condition is not generally fulfilled, and the current‐value Hamiltonian under (over) estimates the true welfare level by an amount equal to the discounted value of the net “pure time effect.” For the non‐autonomous case of a time‐dependent utility discount rate, a general condition on the discount rate function (of which the hyperbolic discount rate function is a special case) upholds the results obtained for autonomous cases. The paper concludes with a discussion of policies that promote both optimality and sustainability objectives.     

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm(EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system(ANFIS) and artificial neural networks(ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization(PSO)and ant colony optimization for continuous domains(ACO_R) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACO_R methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity(EC), total dissolved solids(TDS), the sodium adsorption ratio(SAR), carbonate hardness(CH), and total hardness(TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACO_R. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.     

Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies

The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacks – notably early convergence, a low rate of convergence, or trapping in local optimizations – that limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide.

     

Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems

Water Resources Management - Reservoirs’ optimal operation is a critical issue in the management of surface water resources. In the present study, after combining the whale optimization...     

Impact of Italian smoking ban on business activity of restaurants,cafés and bars

We estimate the economic impact of the Italian smoking ban of 2005 on the catering sector using a quasi-experimental design. Our study indicates that the Italian smoking ban had a slight negative impact on sales of cafés and restaurants but had no effect on profits, earnings, or employment.