Using LARS ─WG model for prediction of temperature in Columbia City, USA
01 Aug 2019-Vol. 584, Iss: 1, pp 012026
TL;DR: In this article, the Long Ashton Research Station Weather Generator (LARS-WG) model is used for downscaling daily maximum temperatures based on the SRA1B scenario.
Abstract: Climate change has placed considerable pressure on the residential environment in different areas of the world. These issues have increased the motivation of researchers to analyse and forecast the changes in critical climatic factors, such as temperature, in order to offer valuable reference outcomes for management and planning in the future. This study set out to determine to what extent global warming would affect Columbia City, Missouri, USA. The Long Ashton Research Station Weather Generator (LARS-WG) model is used for downscaling daily maximum temperatures based on the SRA1B scenario. Seven General Circulation Models (GCMs) outputs are employed for three selected periods, 2011–2030, 2046–2065 and 2080-2099. The findings show that (1) statistical analysis confirmed the skill and reliability of the LARS-WG model to downscale maximum temperature time series; (2) the ensemble mean of seven GCMs exhibited an increasing based on yearly and monthly data for all periods compared with baseline period 1980-1999. The findings can contribute to a better understanding of the impacts of climate change on the urban environment and encourage planners and stakeholders to find the best solution for mitigation of these impacts.
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TL;DR: In this paper, the authors applied a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption.
Abstract: The proper management of a municipal water system is essential to sustain cities and support the water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Moreover, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth.
122 citations
TL;DR: A novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity, and the performance of the hybrid model SMA-ANN is better than ANN based on the range of statistical criteria.
Abstract: Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.
106 citations
Cites background from "Using LARS ─WG model for prediction..."
...Hence, water 45 utilities should support and enhance the management of the municipal water system [3-5]....
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TL;DR: This research aimed to develop a new EC unit design using drilled plates (electrodes) to mix the solution being treated without using external mixers, this minimising power consumption and the cost of removing iron using the proposed EC unit.
104 citations
01 Jul 2020
TL;DR: In this article, an ultrasonic-assisted electrocoagulation (U-ELE) method was used to remove nitrates from water under various operational conditions, such as initial pH (4.0-8.0), applied current densities (ACD) (6.0 -9.0 mA/cm2), flow rates (FR) (60-100 ml/min), and initial nitrate concentrations (INC) (100-200 mg/L), which were optimized using the Central Composite Design (CCD).
Abstract: Water contamination with nitrates is a serious problem due to the detrimental effects of nitrates on both human life and the global ecosystem; therefore, it is essential to remove nitrates using efficient methods. Accordingly, various methods have been used to treat nitrate-containing solutions, but recent studies focused on electrocoagulation (ELE) as it produces high quality water at low cost and it is environmentally friendly. However, passive layers are growing on the aluminum anodes after short time of treatment, which substantially affects the efficiency of ELE. In this investigation therefore, ultrasonic filed was used to remove these passive layers, and consequently improves the efficiency of ELE. This new method, ultrasonic-assisted ELE (U-ELE), was used to remove nitrates from water under various operational conditions. In particular, the impacts of water initial pH (WIP) (4.0-8.0), applied current densities (ACD) (6.0-9.0 mA/cm2), flow rates (FR) (60-100 ml/min), and initial nitrate concentrations (INC) (100-200 mg/L), which were optimized using the Central Composite Design (CCD). The ultrasonic irradiation time (UT) has been kept at 10 minutes for all experiments. The best possible removal of nitrate using only ELE method was about 77% at WIP of 6, UT of 10 minutes, FR of 40 ml/min, INC of 150 mg/l and ACD of 7.5mA/cm2. However, it was found that exerting ultrasonic for 10 minutes, U-ELE method, has increased nitrates removal to 87.80% under the same conditions of ELE treatment.
76 citations
01 Jul 2020
55 citations
References
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01 Jan 1997
TL;DR: This informal consolidated text of the Kyoto Protocol incorporates the Amendment adopted at the eighth session of the Conference of the Parties serving as the meeting of the parties to Kyoto Protocol (Doha Amendment).
Abstract: ____________________________________________ *This informal consolidated text of the Kyoto Protocol incorporates the Amendment adopted at the eighth session of the Conference of the Parties serving as the meeting of the Parties to the Kyoto Protocol (Doha Amendment). The Doha Amendment has not, as yet, entered into force. The informal consolidated text therefore has no official legal status and has been prepared by the secretariat solely to assist Parties. 1 KYOTO PROTOCOL TO THE UNITED NATIONS FRAMEWORK CONVENTION ON CLIMATE CHANGE*
5,435 citations
TL;DR: In this article, the authors present an approach to evaluate global climate simulations and to downscale global climate scenarios for the assessment of climate impacts on hydrologic systems in the Pacific Northwest, USA.
Abstract: This paper reviews methods that have been used to evaluate global climate simulations and to downscale global climate scenarios for the assessment of climate impacts on hydrologic systems in the Pacific Northwest, USA. The approach described has been developed to facilitate integrated assessment research in support of regional resource management. Global climate model scenarios are evaluated and selected based on historic 20 th Century simulations. A statistical downscaling method is then applied to produce a regional data set. To facilitate the use of climate projections in hydrologic assessment, additional statistical mapping may be applied to generate synthetic station time series. Finally, results are presented from a regional climate model that indicate important differences in the regional climate response from what is captured by global models and statistical downscaling. 1. Introduction Some of the most important anticipated impacts of climate change are expressed through hydrologic processes such as streamflow, snowpack, and flooding. Modeling these impacts requires high- resolution regional data for future scenarios of temperature and precipitation. The science of climate change at global and regional scales is quite advanced and climate simulations are typically downscaled to as fine as 10-50 km grids or to station locations. While there remains significant research to be done to fully understand climate dynamics at these scales and to bolster confidence in future scenarios, the current climate modeling is adequate for many applications in hydrology. A principal challenge is linking global climate simulations to existing computational tools and institutional mechanisms within an integrated assessment. For example, under global climate change, system impact assessment is complicated by the constantly shifting underlying climate trends within large year-to-year variability (Arnell, 1996). The analysis of water resource systems and their reliability, yield, and specific event frequency, generally assumes a static state that can be described statistically using a time series of historic events and depends on using the observed record of the past to estimate the probability of future events. The observed record is assumed to be statistically stationary so that all events are equally probable and these probabilities are assumed to carry into the future. Typically, climate projections are based on transient simulations from multiple projected emissions scenarios and climate models. While this approach can generate a large number of projections based on various models and emissions scenarios, it does not correspond well to the current approach in resource management. This paper reviews methods developed by the Climate Impacts Group (CIG) at the University of Washington for integrated assessment of climate change impacts in the Pacific Northwest, United States. This research focuses on four diverse yet connected natural systems of the Pacific Northwest (fresh water, forests, salmon and coasts) and the socioeconomic and/or political systems associated with each. Hydrologic processes are central to the climate impacts in all sectors; thus, downscaling climate scenarios for hydrologic simulations forms the basis for quantitative analyses. Many of the approaches we have developed are based on empirical corrections to simulated climate data. These corrections are based on a relationship between the observed statistics of a parameter and the simulation of that parameter for equivalent climate conditions. This relationship is then used to correct the simulation of that parameter for future climate conditions. In its simplest form, that relationship could be a simple perturbation to correct a bias. In the quantile mapping, however, the full probability distribution is taken into account. For example, the temperature simulated by a given model for present-day conditions at a given location may be 5°C too cold compared with observations. For the future climate, one would add 5°C to all values simulated at that location to correct this bias. The bias may be simply a lapse-rate correction for unresolved topography or it may stem from a deficiency in the
201 citations
"Using LARS ─WG model for prediction..." refers background in this paper
...Several approaches have been utilised to assess global climate simulations and to downscale different global climate scenarios, resulting from different emissions scenarios, for the evaluation of climate influences on hydrologic systems [18, 19, 23]....
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TL;DR: In this article, the authors investigated the removal of phosphate from water using a new baffle plates aluminium-based electrochemical cell (PBPR) taking consideration the influence of key operating parameters.
173 citations
"Using LARS ─WG model for prediction..." refers background in this paper
...cholera and malaria; 3) depilation the freshwater resources which in turn exerts extra pressure on water and wastewater treatment facilities [4-14]....
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TL;DR: The obtained results indicated that FCER reduced the iron concentration from 20 to 0.3 mg/L within 20 min of electrolysis at initial pH of 6, inter-electrode distance (ID) of 5 mm, current density (CD) of 1.5 mA/cm2, and minimum operating cost of 0.22 US $/m3.
165 citations
"Using LARS ─WG model for prediction..." refers background in this paper
...cholera and malaria; 3) depilation the freshwater resources which in turn exerts extra pressure on water and wastewater treatment facilities [4-14]....
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TL;DR: FCER reduces the need for external stirring and aerating devices, which until now have been widely used in the electrocoagulation reactors, and could be a promising cost-effective alternative to the traditional lab-scale EC reactors.
129 citations
"Using LARS ─WG model for prediction..." refers background in this paper
...cholera and malaria; 3) depilation the freshwater resources which in turn exerts extra pressure on water and wastewater treatment facilities [4-14]....
[...]