Sustainability of Groundwater Resources in the North-Eastern Region of Bangladesh

Water is essential for economic, social, and environmental development. Global water resources are vulnerable due to increasing demand related to population growth, pollution potential, and climate change. Competition for water between different sectors is increasing. To meet the increasing demand, the use of groundwater is increasing worldwide. In this paper, the water-table dynamics of the north-eastern region of Bangladesh were studied using the MEKESENS software. This study reveals that the depth to water-table (WT) of almost all the wells is declining slowly. In many cases, the depth will approximately double by the year 2040, and almost all will double by 2060, if the present trend continues. If the decline of the water-table is allowed to continue in the long run, the result could be a serious threat to the ecology and to the sustainability of food production, which is vital for the nation’s food security. Therefore, necessary measures should be taken to sustain water resources and thereby agricultural production. Demand-side management of water and the development of alternative surface water sources seem to be viable strategies for the area. These strategies could be employed to reduce pressure on groundwater and thus maintain the sustainability of the resource.     

Gene-expression programming for flip-bucket spillway scour

During the last two decades, researchers have noticed that the use of soft computing techniques as an alternative to conventional statistical methods based on controlled laboratory or field data, gave significantly better results. Gene-expression programming (GEP), which is an extension to genetic programming (GP), has nowadays attracted the attention of researchers in prediction of hydraulic data. This study presents GEP as an alternative tool in the prediction of scour downstream of a flip-bucket spillway. Actual field measurements were used to develop GEP models. The proposed GEP models are compared with the earlier conventional GP results of others (Azamathulla et al. 2008b; RMSE = 2.347, δ = 0.377, R = 0.842) and those of commonly used regression-based formulae. The predictions of GEP models were observed to be in strictly good agreement with measured ones, and quite a bit better than conventional GP and the regression-based formulae. The results are tabulated in terms of statistical error measures (GEP1; RMSE = 1.596, δ = 0.109, R = 0.917) and illustrated via scatter plots.     

An ANFIS-based approach for predicting the bed load for moderately sized rivers

A total of 346 sets of bed-load data obtained from the Kinta River, Pari River, Kerayong River and Langat River were analyzed using four common bed-load equations. These assessments, based on the median sediment size (d₅₀), show that the existing equations were unable to predict the measured bed load accurately. All existing equations over-predicted the measured values, and none of the existing bed-load equations gave satisfactory performance when tested on local river data. Therefore, the present study applies a new soft computing technique, i.e. an adaptive neuro-fuzzy inference system (ANFIS), to better predict measured bed-load data. Validation of the developed network (ANFIS) was performed using a new set of bed-load data collected at Kulim River. The results show that the recommended network can more accurately predict the measured bed-load data when compared to an equation based on a regression method.     

Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat

This study demonstrates that a combination of bulk NMR and magnetic resonance imaging measurements of the T(2)-values of water protons can be used to determine the heat-induced changes in the structure and moisture content of fresh chicken meat which had been cooked in a convection oven at 200°C for a range of times. The gravimetric moisture content was also determined for both the raw and cooked meat. Multi-exponential fitting of the bulk NMR T(2) relaxation time data demonstrated three distinct water populations T(21) (39-43ms), T(22) (82-99ms) and T(23) (2-3ms) for raw meat which changed to 18-31ms (T(21)), 61-208ms (T(22)) and 3-7ms (T(23)) after the meat had been cooked. The T(1) and T(2) values obtained by MRI for cooked meat decreased progressively with increased heating time. There are highly significant correlations between the T(2) values from MRI and the T(21) values from bulk NMR measurements of cooked meat (r=0.986; p<0.01), and also between the normalised M(0) values from MRI and the gravimetric moisture content (r=0.953; p<0.01).