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Barzegar Rahim Aalami Mohammad Taghi Adamowski Jan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2020,34(2):415-433
Stochastic Environmental Research and Risk Assessment - Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely... 相似文献
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Bahare Raheli Mohammad Taghi Aalami Ahmed El-Shafie Mohammad Ali Ghorbani Ravinesh C. Deo 《Environmental Earth Sciences》2017,76(14):503
Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO4), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH4-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems. 相似文献
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Rahman Khatibi Mohammad Ali Ghorbani Mohammad Taghi Aalami Kasim Kocak Oleg Makarynskyy Dina Makarynska Mahdi Aalinezhad 《Ocean Dynamics》2011,61(11):1797-1807
Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive
management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys
Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed:
low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which
describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem
suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation
dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h
or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific
dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another,
the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight
from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual
insights provided by each model. 相似文献
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Trend analysis of groundwater using non-parametric methods (case study: Ardabil plain) 总被引:1,自引:0,他引:1
Farnaz Daneshvar Vousoughi Yagob Dinpashoh Mohammad Taghi Aalami Deepak Jhajharia 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(2):547-559
In the present study, the trends in groundwater level and fifteen hydro-geochemical elements at 32 piezometric stations located in the Ardabil plain of the northwest of Iran were analyzed using the non-parametric Mann–Kendall method after removing the effect of significant lag-1 serial correlation from the respective time series by pre-whitening. The magnitudes of trends were computed using the Sen’s estimator method. The homogeneity of trend was tested using the method proposed by van Belle and Hughes as well. Results showed that significant (α < 0.1) negative trends in groundwater level were witnessed for all but five stations of the Ardabil plains during the last 22 years from 1988 to 2009. The groundwater levels over Ardabil plain have declined at the rate of about 18 cm/year, with the strongest decline (1.93 m/year) witnessed at Khalife-loo-sheikh station. The results of homogeneity of trends showed that trends were homogeneous for months but not for stations. Strong positive trends were detected in the groundwater quality concentration across the whole plain. Decline in groundwater level and increase in geochemical elements in the groundwater were attributed to the human activities in the Ardabil plain located in the northwest of Iran. 相似文献
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