Analysis of socioeconomic and environmental growth of Muzaffarpur city using a novel rainfall and flood forecasting model.
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| Title: | Analysis of socioeconomic and environmental growth of Muzaffarpur city using a novel rainfall and flood forecasting model. |
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| Authors: | Ali, Md Arman |
| Source: | Soft Computing - A Fusion of Foundations, Methodologies & Applications; Sep2024, Vol. 28 Issue 17/18, p10669-10688, 20p |
| Subject Terms: | ARTIFICIAL neural networks, RAINFALL, FLOOD forecasting, STANDARD deviations, ECONOMIC expansion |
| Abstract: | Assessing the growth of social, environmental, and economic domains are too difficult task. Thus, an accurate prediction could not be made via the conventional prediction method. Hence, a novel Goat-based Artificial Neural System (GbANS), in order to provide improved growth visualization of socioeconomic and environmental behavior prediction. Moreover, the study region selected for this validation is Muzaffarpur City and its environs. Using the trained rainfall and flood data, the initial phase's flood and rainfall rates were estimated. The growth rate of environmental and socioeconomic behaviors was projected based on the condition. Here, the goat's best solution is used to analyze the growth in the socioeconomic and environmental domains. In the present research, the novelty lies in the integration of goat optimization with the artificial neural network for improved forecasting of rainfall and floods. Also, GbANS goes beyond simply predicting rainfall and floods. It incorporates modules to assess the potential socioeconomic and environmental impacts of the predicted flood. The model holds the most promising solution for accurate rainfall and flood prediction. GbANS has the potential to be a significant advancement in rainfall and flood forecasting due to its potential for improved accuracy and enhanced decision-making support. The performance characteristics of the developed GbANS were then assessed and compared in terms of accuracy, Mean Square Error (MSE), and Root Mean Square Error (RMSE) with other models that were already in use. The suggested method has achieved an enhanced best result with an accuracy of 98.1%. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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