Depth measurement is Considered as a first goal in hydrographic survey, it depends on different techniques and instruments, it’s most costly procedures. However; some mathematical models are used for condensing depths with a relatively low cost. Artificial neural networks appears and in many applications used to solve real-world forecasting, classification and function approximation problems. It is fast, intelligent and easy to use Neuro Intelligence supports all stages of neural network application. The objective of this research is to test the possibility of using such a method for depth prediction, and comparing with the geographical information system models. It is found that artificial neural networks statistically give abetter results.
Abdalla, K (2021). A Comparison of Depth Interpolation by Using GIS & Neural Networks. Afribary. Retrieved from https://track.afribary.com/works/a-comparison-of-depth-interpolation-by-using-gis-neural-networks
Abdalla, Khalid "A Comparison of Depth Interpolation by Using GIS & Neural Networks" Afribary. Afribary, 11 May. 2021, https://track.afribary.com/works/a-comparison-of-depth-interpolation-by-using-gis-neural-networks. Accessed 27 Nov. 2024.
Abdalla, Khalid . "A Comparison of Depth Interpolation by Using GIS & Neural Networks". Afribary, Afribary, 11 May. 2021. Web. 27 Nov. 2024. < https://track.afribary.com/works/a-comparison-of-depth-interpolation-by-using-gis-neural-networks >.
Abdalla, Khalid . "A Comparison of Depth Interpolation by Using GIS & Neural Networks" Afribary (2021). Accessed November 27, 2024. https://track.afribary.com/works/a-comparison-of-depth-interpolation-by-using-gis-neural-networks