Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images
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 dept...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Pouyan Press
2019-04-01
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| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | http://www.jsoftcivil.com/article_95794_6168570e23ad71388e53ddc280fe1622.pdf |
| _version_ | 1818614758232817664 |
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| author | Amin Jalilzadeh Saeed Behzadi |
| author_facet | Amin Jalilzadeh Saeed Behzadi |
| author_sort | Amin Jalilzadeh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T16:23:06Z |
| format | Article |
| id | doaj.art-101a235a8f464e809f7313e43c4c57b7 |
| institution | Directory Open Access Journal |
| issn | 2588-2872 2588-2872 |
| language | English |
| last_indexed | 2024-12-16T16:23:06Z |
| publishDate | 2019-04-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj.art-101a235a8f464e809f7313e43c4c57b72022-12-21T22:24:51ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722588-28722019-04-0132546410.22115/scce.2019.196533.111995794Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing ImagesAmin Jalilzadeh0Saeed Behzadi1M.Sc. Student in Geographic Information Systems, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, IranAssistant Professor in Surveying Engineering, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, IranKnowing 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.http://www.jsoftcivil.com/article_95794_6168570e23ad71388e53ddc280fe1622.pdfremote sensinggeographic information systems (gis)artificial computationbathymetry |
| spellingShingle | 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 remote sensing geographic information systems (gis) artificial computation bathymetry |
| title | Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images |
| title_full | Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images |
| title_fullStr | Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images |
| title_full_unstemmed | Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images |
| title_short | Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images |
| title_sort | machine learning method for predicting the depth of shallow lakes using multi band remote sensing images |
| topic | remote sensing geographic information systems (gis) artificial computation bathymetry |
| url | http://www.jsoftcivil.com/article_95794_6168570e23ad71388e53ddc280fe1622.pdf |
| work_keys_str_mv | AT aminjalilzadeh machinelearningmethodforpredictingthedepthofshallowlakesusingmultibandremotesensingimages AT saeedbehzadi machinelearningmethodforpredictingthedepthofshallowlakesusingmultibandremotesensingimages |