DOI•
Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model
02 Sep 2021-Vol. 877, Iss: 1, pp 012033
TL;DR: In this paper, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature, where the temperature time series is decomposed into three components and the trend component is subjected for modelling.
Abstract: In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings.
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TL;DR: In this article, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested, and the results indicate that coupled wavelet-neural network models are a potentially promising new method of urban water forecast that merit further study.
Abstract: [1] Daily water demand forecasts are an important component of cost-effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA-ANN models for urban water demand forecasting at lead times of one day for the summer months (May to August) were developed, and their relative performance was compared using the coefficient of determination, root mean square error, relative root mean square error, and efficiency index. The key variables used to develop and validate the models were daily total precipitation, daily maximum temperature, and daily water demand data from 2001 to 2009 in the city of Montreal, Canada. The WA-ANN models were found to provide more accurate urban water demand forecasts than the MLR, MNLR, ARIMA, and ANN models. The results of this study indicate that coupled wavelet-neural network models are a potentially promising new method of urban water demand forecasting that merit further study.
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TL;DR: A combined Cubist and residual kriging approach can be considered the best solution for predicting spatial temperature patterns based on a network of temperature observation plots across the southern slopes of Mt. Kilimanjaro.
Abstract: Spatially high resolution climate information is required for a variety of applications in but not limited to functional biodiversity research. In order to scale the generally plot-based research findings to a landscape level, spatial interpolation methods of meteorological variables are required. Based on a network of temperature observation plots across the southern slopes of Mt. Kilimanjaro, the skill of 14 machine learning algorithms in predicting spatial temperature patterns is tested and evaluated against the heavily utilized kriging approach. Based on a 10-fold cross-validation testing design, regression trees generally perform better than linear and non-linear regression models. The best individual performance has been observed by the stochastic gradient boosting model followed by Cubist, random forest and model averaged neural networks which except for the latter are all regression tree-based algorithms. While these machine learning algorithms perform better than kriging in a quantitative evaluation, the overall visual interpretation of the resulting air temperature maps is ambiguous. Here, a combined Cubist and residual kriging approach can be considered the best solution.
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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.
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TL;DR: In this paper, a hybrid approach by combining the past performance and the envelope methods has been proposed for the selection of an ensemble of general circulation models (GCMs) of Couple Model Intercomparison phase 5 (CMIP5) for the projection of spatiotemporal changes in annual and seasonal temperatures of Iraq for four representative concentration pathways (RCP) scenarios.
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Abstract: A new electrocoagulation reactor (EC), which utilises the concepts of baffle-plates, has been applied to remove Escherichia coli (E. coli) from wastewater. This new aluminium-based EC reactor utilises perforated baffle-plates electrodes to mix water, which reduces the need for mechanical or magnetic mixers that require extra power to work. This new reactor has been used to treat E. coli containing wastewater samples, considering the effects of different parameters such as treatment time (TT), inter-electrode distance (IED), and current density (CD). A statistical analysis has also been commenced to evaluate the influence of each parameter on the removal of E. coli. Additionally, an economic study has been conducted to assess the operating cost of the new reactor. The outcomes of the experimental work confirmed that the new reactor removes as high as 96 % of the E. coli within 20 min of electrolysis at IED of 0.5 cm, and CD of 1.5 m A / c m 2 . Additionally, it has been found that the operating cost of the new reactor is 0.11 US $/m3 (for E. coli removal), which is less than operating cost of traditional reactors. Finally, it has been found that the effect of the studied parameters on E. coli removal followed the order: T T > C D > I E D .
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