Reusable rainwater quality at Ikorodu area of Lagos, Nigeria: Impact of first-flush and household treatment techniques

YesWater scarcity is a huge problem in Africa, and hence rainwater becomes a crucial water source for fulfilling basic human needs. However, less attention has been given by African countries to the effectiveness of common rainwater treatments to ensure the population's health. This study investigates the impact of different household treatment techniques (HHTTs), i.e. treatments by chlorine, boiling, alum, and a combination of alum and chlorine, on its storage system using a case study at the Ikorodu area of Lagos state, which is a rural area in Nigeria. The first-flush quality has been particularly studied here, where the microbial reduction through its practice has been examined from five different roofs. One of the investigated roofs was from a residential building, and four were constructed for the purpose of this study. In this study, the physical parameters (i.e. total suspended solids and turbidity) and the microbial parameters (i.e. total coliform and Escherichia coli) of the collected rainwater have been investigated. From the results, it has been observed that: (1) the water quality at the free phase zone is better than that at the tank's bottom; (2) the combination of chlorine and alum gives the best rainwater quality after comparing the application of different HHTTs; and (3) a reduction of about 40% from the original contaminant load occurs in every 1 mm diversion.Hidden Histories of Environmental Science Grant Project (at Seed-grant Stage), funded by the Natural Environment Research Council (NERC) and Arts and Humanities Research Council (AHRC), part of UK Research and Innovation (UKRI

Prediction of scour caused by 2D horizontal jets using soft computing techniques

This paper presents application of five soft-computing techniques, artificial neural networks, support vector regression, gene expression programming, grouping method of data handling (GMDH) neural network and adaptive-network-based fuzzy inference system, to predict maximum scour hole depth downstream of a sluice gate. The input parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, jet Froude number and the tail water depth. Six non-dimensional parameters were achieved to define a functional relationship between the input and output variables. Published data were used from the experimental researches. The results of soft-computing techniques were compared with empirical and regression based equations. The results obtained from the soft-computing techniques are superior to those of empirical and regression based equations. Comparison of soft-computing techniques showed that accuracy of the ANN model is higher than other models (RMSE = 0.869). A new GEP based equation was proposed

Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia

Abstract Climate change is not a myth. There is enough evidence to showcase the impact of climate change. Town planners and authorities are looking for potential models to predict the climatic factors in advance. Being an agricultural area in Saudi Arabia, Tabuk region gets greater interest in developing such a model to predict the atmospheric temperature.Therefore, this paper presents two different studies based on artificial neural networks (ANNs) and gene expression programming (GEP) to predict the atmospheric temperature in Tabuk. Atmospheric pressure, rainfall, relative humidity and wind speed are used as the input variables in the developed models. Multilayer perceptron neural network model (ANN model), which is high in precession in producing results, is selected for this study. The GEP model that is based on evolutionary algorithms also produces highly accurate results in nonlinear models. However, the results show that the GEP model outperforms the ANN model in predicting atmospheric temperature in Tabuk region. The developed GEP-based model can be used by the town and country planers and agricultural personals. Graphical abstrac

Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models

Flip-bucket spillways are utilized in hydraulic engineering to diminish the kinetic energy of flowing water by redirecting the flow jet into the air. In the downstream stailing basin with low tail-water, sediment particles movement results in scour hole formation, posing a threat to spillway stability. The accurate prediction of scour hole depth is a crucial area of the present research work. This study endeavors to employ four data-driven models (DDMs), namely Support Vector Machine (SVM), Gene Expression Programming (GEP), Multilayer Perceptron (MLP), and Multivariate Adaptive Regression Splines (MARS), in combination with five selected empirical equations. The objective is to accurately predict scour depth utilizing field-collected data from site number 84. Relative scour depth, dsH1, was simulated based on the readily extracted parameter i.e. Froude number, Fr=qgH13. The evaluation of model performance was conducted using fundamental metrics, including root mean square error (RMSE), coefficient of determination (R2), mean average error (MAE), and the maximum value of the developed discrepancy ratio (DDRmax). Among the DDMs, the MARS model demonstrated superior performance in both the training and testing phases. In the training phase, it yielded metrics (RMSE = 0.08665, MAE = 0.05714, R2 = 0.99169, DDRmax = 4.519), and in the testing phase, it produced metrics (RMSE = 0.0252, MAE = 0.0170, R2 = 0.09933, DDRmax = 9.144). This exceptional performance of the MARS model surpassed the initially selected (Wu, 1973) [1] experimental model, which exhibited metrics (RMSE = 0.39667, MAE = 0.17463, R2 = 0.96172, DDR = 1.428). The evaluation indices conclusively establish the MARS method's absolute superiority over the experimental approach proposed by Wu (1973) [1]