Municipal solid waste (MSW) management remains a challenge in developing countries due to increasing waste generation, high costs associated with waste management and the structure of the containment systems implemented. This study analyses the classification of landfilling systems by using documented cases reported mainly in publications in waste management in relation to nonengineered landfilling systems/approved dumpsites in Sub Saharan African (SSA) countries from 2000 to 2018. The work identifies an existing system for the classification of landfill sites and utilises this system to determine the situation of landfill sites in SSA countries. Each article was categorised according to the main landfilling management practice reported: Uncontrolled dumping, semi controlled facility, medium controlled facility, medium/high-engineered facility or high state-of theart facility. Findings suggested that 80% of the documented cases of landfill sites assessed in SSA countries were classified as level 0 or 1. The structure of the containment and controlled regime were identified by the focus group discussion participants as important predictors of possible strengths, weaknesses, opportunities and threats for the landfill sites considered. The study represents the first identifiable and comprehensive academic evaluation of landfill site classification based on site operations reported in the available peer reviewed literature. The information provides insight on the status of landfill sites in SSA countries with respect to the landfilling management practice and a baseline for alternative corrective measures.
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.
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.
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