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This study used data from the 2008 Ghana Demographic and Health Survey to investigate the association between selected socio-demographic factors and dietary behaviour as measured by fruit and vegetable consumption among a sample of 6139 young people aged 15-34 years in Ghana. Overall, fruit and vegetable consumption was low in young people, but females were likely to consume more fruit and vegetables than their male counterparts. Respondents from the Mande ethnic group, those who resided in rural areas and those living in the Brong/Ahafo, Ashanti and the Eastern regions consumed more fruit and vegetables than those from other regions. Females who were Catholic/Anglican, Methodist/Presbyterian and Pentecostal/Charismatic were more likely than those of other religions to consume fruit and vegetables, while Muslim males generally consumed more fruit and vegetables. The findings point to the need for interventions to educate young people in Ghana about the health benefits of eating fruit and vegetables.

Many attempts have been made in the recent past to model and forecast streamflow using various techniques with the use of time series techniques proving to be the most common. Time series analysis plays an important role in hydrological research. Traditionally, the class of autoregressive moving average techniques models has been the statistical method most widely used for modelling water discharge, but it has been shown to be deficient in representing nonlinear dynamics inherent in the transformation of runoff data. In contrast, the relatively newly improved and efficient soft computing technique artificial neural networks has the capability to approximate virtually any continuous function up to an arbitrary degree of accuracy, which is not otherwise true of other conventional hydrological techniques. This technique corresponds to human neurological system, which consists of a series of basic computing elements called neurons, which are interconnected together to form networks. The aim of the study is to compare the artificial neural network and autoregressive integrated moving average to model River Opeki discharge (1982–2010) and to use the best predictor to forecast the discharge of the river from 2010 to 2020. The performance of the two models was subjected to statistical test based on correlation coefficient (r) and the root‐mean‐square error. The result showed that autoregressive integrated moving average performs better considering the level of root‐mean‐square error and higher correlation coefficient.

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