(2016). Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine. Journal of Hydrology, Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine AbstractIn this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known analytical solutions. Predicted results obtained by the hybrid methods are closer to the field data compared to the outputs of analytical and simple ANN models. Results show that despite the use of fewer and simpler parameters by the hybrid models, the ANN-GA and to some extent the ANN-SA have the ability to compete with the numerical models. AbstractIn this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known ana...

Sweetpotato is used in various food preparations in place of rice, cassava, yam and plantain in Ghana. In spite of this it does not have the same importance in Ghanaian diet as other root and tuber crops. Consumer taste, preference and acceptance are critical in determining the suitability of sweetpotato cultivars to any locality. A study was carried out in some selected communities of Ghana where sweetpotato is popular which span all five ecozones of Ghana in February, 2012. The main objective was to investigate why sweetpotato has low utilization compared with other root and tuber crops and to increase its utilization through breeding. The study employed Focus Group Discussion (FGD) followed by administration of Semi-structured Questionnaire (SSQ). Data collected were analysed using Genstat and Statistical Package for Social Sciences. Seventy-nine people consisting of 63% males and 37% females, and 178 people consisting of 52% female and 48% males were involved in the FGD and SSQ, respectively. Majority (94%) of farmers' ranked sweetpotato from 1 to 5 among 24 cultivated crops. Only about 28% of consumers ate sweetpotato at least six days per week. The survey revealed that consumers in Ghana desired non-sweet, high dry matter sweetpotato cultivars. Therefore, there is need for Research and Development to adjust sweetpotato breeding objectives and selection procedures to develop high dry matter non-sweet sweetpotato varieties in Ghana.

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Sweetpotatoes utilization is low in Ghana due to lack of farmer and consumer preferred cultivars. Poor flowering and incompatibilities among genotypes limit breeding progress in its improvement. The objective was to assess compatibilities among sweetpotato genotypes to select good parents for breeding end-user preferred varieties for increased utilization. Twenty-one genotypes selected from 115 accessions evaluated across three contrasting environments were crossed using full diallel mating scheme. In all, 6388 crosses were carried out and 3214 seeds produced. This study sought to understand the genetic incompatibilities based on the number of seeds set per capsule after self-or cross-fertilization. Lack of flowering or poor flowering, and self-and cross-incompatibilities were major constraints to sweetpotato improvement found. Four genotypes (Histarch, Apomuden, Beauregard, and Ogyefo) were the best parents based on cross compatibilities and they can be used to determine the genetic control of beta-carotene, dry matter and sugar content in sweetpotato. Histarch and Ogyefo are recommended as parents for the development of non-sweet, high dry matter sweetpotato varieties that are the preferred cultivars in Ghana because of their low sugar content. Use of many genotypes in hybridization and establishment of crossing blocks in the minor cropping season is highly recommended.

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