Department of Civil Engineering, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran
10.22115/scce.2019.196533.1119
Abstract
Knowing the lake’s characteristics information such as depth is an essential requirement for the water managers; however, conducting a comprehensive bathymetric survey is considered as a difficult task. After the advent of remote sensing, and satellite imagery, it has been recognized that water depth can be estimated in some way over shallow water. There are many models that can evaluate relationships between multi-band images, and depth measurements; however, artificial computation methods can be used as an approximation tool for this issue. They are also considered as fairly simple and practical models to estimate depth in shallow waters. In this paper, different methods of artificial computation are used to calculate the depth of shallow lake, then these methods are compared. The results show that Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), and regression learner are best methods for this issue with RMSE 0.8 , 1.47, and 0.96 respectively.
Jalilzadeh, A., Behzadi, S. (2019). Machine Learning Method for predicting the depth of shallow lakes Using Multi-Band Remote Sensing Images. Journal of Soft Computing in Civil Engineering, 3(2), 59-68. doi: 10.22115/scce.2019.196533.1119
MLA
Amin Jalilzadeh; Saeed Behzadi. "Machine Learning Method for predicting the depth of shallow lakes Using Multi-Band Remote Sensing Images". Journal of Soft Computing in Civil Engineering, 3, 2, 2019, 59-68. doi: 10.22115/scce.2019.196533.1119
HARVARD
Jalilzadeh, A., Behzadi, S. (2019). 'Machine Learning Method for predicting the depth of shallow lakes Using Multi-Band Remote Sensing Images', Journal of Soft Computing in Civil Engineering, 3(2), pp. 59-68. doi: 10.22115/scce.2019.196533.1119
VANCOUVER
Jalilzadeh, A., Behzadi, S. Machine Learning Method for predicting the depth of shallow lakes Using Multi-Band Remote Sensing Images. Journal of Soft Computing in Civil Engineering, 2019; 3(2): 59-68. doi: 10.22115/scce.2019.196533.1119
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