Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.     

Response of flower and boll development to climatic factors in Egyptian cotton (Gossypium barbadense)

Fruiting of cotton plant is determined and influenced by cultivars, climatic conditions, management practices and pests. An understanding of the flowering and boll retention patterns of cotton cultivars can contribute to more efficient and economical crop management. The objective of this investigation was to study the effect of various climatic factors on flower and boll production, and also, the nature of its effects prevailing prior and subsequent to either flowering or boll setting on flower and boll production and retention in Egyptian cotton. This could be used in formulating advanced prediction of the effect of certain climatic conditions on the production of Egyptian cotton. Also, the study focused on four equal periods during the development of flower and bolls stage to study the response of these characters to climatic factors during these periods and to determine the most representative period corresponding to the overall crop pattern. Further, the study predicting effects of climatic factors during convenient intervals (in days) on cotton flower and boll production compared with daily observations to find the optimum interval. Evaporation, sunshine duration, humidity, surface soil temperature at 1800 h, and maximum air temperature, are the important climatic factors that significantly affects flower and boll production. Evaporation; minimum humidity and sunshine duration were the most effective climatic factors during preceding and succeeding periods on boll production and retention. There was a negative correlation between flower and boll production and either evaporation or sunshine duration, while that correlation with minimum humidity was positive. The fourth quarter period of the production stage was the most appropriate and usable time to collect data for determining efficient prediction equations for cotton production. Evaporation, humidity and temperature were the principal climatic factors that governed cotton flower and boll production during the fourth quarter. The five day interval was found to be adequately and sensibly related to yield parameters than other intervals and was closest to the daily observations. Evaporation was found to be the most important climatic variable affecting flower and boll production, followed by humidity. An accurate weather forecast 5–7 days in advance would provide an opportunity to avoid adverse effects of climatic factors on cotton production through utilizing proper cultural practices which would limit and control their negative effects.