Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach

This work is dedicated to conducting a comprehensive analysis of a wheat dataset to identify significant attributes for accurate grain classification. The initial dataset contains various parameters of wheat grains, such as length, perimeter, area, compactness, and asymmetry coefficient. The focus of the study is on analyzing the relationships between these attributes and their impact on the classification target field. Initially, data normalization was performed to eliminate the influence of scale differences between variables. This was followed by a correlation analysis, which revealed several key relationships between attributes. Specifically, it was found that the asymmetry coefficient has a moderate positive correlation with the classification target, while the attributes area, compactness, perimeter, and width exhibit a moderate negative correlation. Length, on the other hand, shows a weak negative correlation with the target attribute. To gain a deeper understanding of the data structure, Kohonen Self-Organizing Maps were used, which helped to identify three clusters. The analysis revealed that the most significant attributes for clustering are compactness, width, perimeter, and area, while the asymmetry coefficient was found to be the least significant. In conclusion, two classification models were built and evaluated. The first model included all attributes from the dataset and demonstrated an accuracy of 0.97. The second model used a subset of attributes excluding the asymmetry coefficient and showed a slightly higher accuracy of 0.98. These results confirm that excluding less significant attributes can lead to a minor but noticeable improvement in model accuracy. Overall, the work highlights the importance of selecting the right attributes to enhance the effectiveness of classification models.

Intelligent approaches to wheat grain classification using neural networks in the agricultural sector

Kozlova,

Khudyakova,

Tynchenko

et al. 2024

Using data mining techniques to identify the impact of water availability in the agricultural sector

Tynchenko,

Krasovskaya,

Kozlova

et al. 2024

This paper comprehensively analyzes the impact of water availability on the agricultural sector, focusing on several key aspects. The study evaluated soil composition and its relationship to crop yields and productivity, as well as the efficiency of different irrigation methods. Special attention was paid to seasonal changes in crop production and their dependence on water resources. The analysis also covered economic indicators, including crop prices and water costs. The used methods of data mining allowed to study in depth the impact of water availability on the agricultural sphere. Correlation analysis revealed that water availability has a significant impact on crop yields and productivity, but this impact is not decisive. In some cases, other factors such as soil composition and agronomic management practices applied played a more important role. Factor analysis identified key factors affecting crop yield, including not only water availability but also soil type, climatic conditions and agronomic practices. It was found that although water availability has an impact on production, it is by no means always the main determinant. The clustering of data showed that there are groups of factors and conditions that together have a complex influence on the results of agricultural production. This confirms that optimization of agricultural activity requires consideration of many variables, not only water resources availability

Machine learning in environmental sustainability factor analysis in the agricultural sector

Kukartsev,

Kozlova,

Kukarceva

2024

The study employed several key data analysis methods aimed at enhancing the understanding of relationships between variables and improving prediction accuracy. The primary tool used was correlation analysis, which allowed for the identification of the degree of association between two variables by determining how changes in one variable relate to changes in another. This established a foundation for further in-depth data analysis. For a deeper understanding and simplified interpretation of the data, factor analysis was utilized. This method helped to identify latent factors that explain the relationships between observed variables and to reduce the number of variables by grouping them. This made the analysis easier and facilitated the identification of key components affecting the data. Logistic regression was applied to build data models. This method is used to model the probability of a specific event occurring based on independent variables, allowing for the classification and prediction of categorical outcomes. The logistic function was used to estimate probabilities and the relationship between the dependent variable and predictors. To enhance the performance of the logistic regression model, a Weight of Evidence (WoE) analysis was conducted. This method converts categorical and continuous variables into numerical formats, simplifying data interpretation and improving the model’s predictive capabilities. WoE analysis helps to identify significant factors, improve the linear relationship between predictors and the dependent variable, and reduce the impact of outliers, which is particularly important in areas such as credit scoring. The results of applying these methods showed that the model based on correlation and factor analysis explained 27.51% of the information on the training set and 76.04% on the test set.