A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing
Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water qua...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/13/2112 |
| _version_ | 1797407961451069440 |
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| author | Mehreen Ahmed Rafia Mumtaz Zahid Anwar Arslan Shaukat Omar Arif Faisal Shafait |
| author_facet | Mehreen Ahmed Rafia Mumtaz Zahid Anwar Arslan Shaukat Omar Arif Faisal Shafait |
| author_sort | Mehreen Ahmed |
| collection | DOAJ |
| description | Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water quality parameter concentrations can be estimated based on the band combinations of the satellite images. In this study, a hybrid remote sensing and deep learning approach for forecasting multi–step parameter concentrations was investigated for the advancement of the traditionally employed water quality assessment techniques. Deep learning models, including a convolutional neural network (CNN), fully connected network (FCN), recurrent neural network (RNN), multi–layer perceptron (MLP), and long short term memory (LSTM), were evaluated for multi–step estimations of an optically active parameter, i.e., electric conductivity (EC), and an inactive parameter, i.e., dissolved oxygen (DO). The estimation of EC and DO concentrations can aid in the analysis of the levels of impurities and oxygen in water. The proposed solution will provide information on the necessary changes needed in water management techniques for the betterment of society. EC and DO parameters were taken as independent variables with dependent parameters, i.e., pH, turbidity, total dissolved solids, chlorophyll–<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, Secchi disk depth, and land surface temperature, which were extracted from Landsat–8 data from the years 2014–2021 for the Rawal stream network. The bi–directional LSTM obtained better results with a root mean square error (RMSE) of 0.2 (mg/L) for DO and an RMSE of 281.741 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>S/cm) for EC, respectively. The results suggest that a hybrid approach provides efficient and accurate results in feature extraction and evaluation of multi–step forecast of both optically active and inactive water quality parameters. |
| first_indexed | 2024-03-09T03:50:23Z |
| format | Article |
| id | doaj.art-494f9264c07b4623b94bbce04304478f |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T03:50:23Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-494f9264c07b4623b94bbce04304478f2023-12-03T14:28:19ZengMDPI AGWater2073-44412022-07-011413211210.3390/w14132112A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote SensingMehreen Ahmed0Rafia Mumtaz1Zahid Anwar2Arslan Shaukat3Omar Arif4Faisal Shafait5School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanDepartment of Computer Science, North Dakota State University (NDSU), Fargo, ND 58102, USACollege of Electrical and Mechanical Engineering (CEME), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanWater is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water quality parameter concentrations can be estimated based on the band combinations of the satellite images. In this study, a hybrid remote sensing and deep learning approach for forecasting multi–step parameter concentrations was investigated for the advancement of the traditionally employed water quality assessment techniques. Deep learning models, including a convolutional neural network (CNN), fully connected network (FCN), recurrent neural network (RNN), multi–layer perceptron (MLP), and long short term memory (LSTM), were evaluated for multi–step estimations of an optically active parameter, i.e., electric conductivity (EC), and an inactive parameter, i.e., dissolved oxygen (DO). The estimation of EC and DO concentrations can aid in the analysis of the levels of impurities and oxygen in water. The proposed solution will provide information on the necessary changes needed in water management techniques for the betterment of society. EC and DO parameters were taken as independent variables with dependent parameters, i.e., pH, turbidity, total dissolved solids, chlorophyll–<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, Secchi disk depth, and land surface temperature, which were extracted from Landsat–8 data from the years 2014–2021 for the Rawal stream network. The bi–directional LSTM obtained better results with a root mean square error (RMSE) of 0.2 (mg/L) for DO and an RMSE of 281.741 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>S/cm) for EC, respectively. The results suggest that a hybrid approach provides efficient and accurate results in feature extraction and evaluation of multi–step forecast of both optically active and inactive water quality parameters.https://www.mdpi.com/2073-4441/14/13/2112deep learningmulti–step forecastingphysico-chemical parameterstime series forecastingwater quality monitoring |
| spellingShingle | Mehreen Ahmed Rafia Mumtaz Zahid Anwar Arslan Shaukat Omar Arif Faisal Shafait A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing Water deep learning multi–step forecasting physico-chemical parameters time series forecasting water quality monitoring |
| title | A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing |
| title_full | A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing |
| title_fullStr | A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing |
| title_full_unstemmed | A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing |
| title_short | A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing |
| title_sort | multi step approach for optically active and inactive water quality parameter estimation using deep learning and remote sensing |
| topic | deep learning multi–step forecasting physico-chemical parameters time series forecasting water quality monitoring |
| url | https://www.mdpi.com/2073-4441/14/13/2112 |
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