فهرست مطالب محسن رشیدیان

Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 satellite images were used to estimate the concentration of acidity, bicarbonate and sulfate parameters. Initially, Sentinel-2 satellite images were pre-processing and then bands and spectral indexes were determined to identify the significant relationship between the parameter values of water quality and images using the multivariate regression method. In the next stage, using Artificial neural network (ANN) and Adaptive Neuro fuzzy inference system (ANFIS) models, the relationship between Sentinel-2 satellite images and water quality parameters were modeled and then their accuracy was calculated for real values. The results showed that in the modeling of sulfate parameter using Sentinel-2 satellite, ANFIS model with relative error equal to 0.0773 and RMSe equal to 0.8014 has a higher accuracy compared to ANN models with relative error equal to 0.1581 and RMSe equal to 1.2477. While, the relative error of the results of the ANN model are obtained 0.0064 and 0.0556 for acidity and bicarbonate parameter, respectively, and RMSe is equal to 0.0702 and 0.2691, respectively.  The ANFIS model has a relative error of 0.0165 and 0.0722, and RMSe is 0.1975 and 0.3037 for acidity and bicarbonate parameter, respectively. Finally, using satellite images, the mentioned models were applied to prepare a qualitative map of each parameter along the part of the Karun river.

Satellite-based precipitation dataset has been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these datasets has limited their application in localized regions and watersheds. So, having an accurate estimation of precipitation by satellites along with the adequate spatial scale in hydrologic studies is the main goal of this study. In this research, Geographically weighted regression (GWR) method was investigated to downscale the Tropical Rainfall Measuring Mission (TRMM-3B42 Version 7) over the DEZ river basin in the southwest of IRAN for 2010-2011. Downscaling was performed based on the non-stationary relationships between the TRMM precipitation and the Digital elevation model (DEM) derived products, the Normalized difference vegetation index (NDVI), the Enhanced vegetation index (EVI) and the Land surface temperature (LST). The result shows that the downscale precipitation at 1 km spatial scale had significantly improved spatial resolution, and agreed well with data from the rain gauge stations. For the 16-day precipitation, Mean square root means square error (RMSE) and absolute mean error (MAE) values are 22.7 mm and 7.45 mm, respectively. However, the accuracy of the model varies in a different location and depends on the vegetation condition.

 Considering the importance of rivers as part of freshwater resources and their role in meeting the needs of agriculture, industry, urban populations, etc., monitoring and predicting the quality of these water resources is essential. These water sources are affected by numerous factors due to their different geological and environmental conditions and their qualitative status also undergoes dramatic changes. However, the quality monitoring of these abundant water resources on the planet's surface is not feasible and requires the use of advanced and powerful tools (Bagherian Marzouni et al., 2014). Due to its capabilities, satellite remote sensing can be used as one of these tools in monitoring water quality and will accurately detect the spatial and temporal changes of these water sources (Bonansea et al., 2015). So far, in many studies, the capabilities of remote sensing satellites to estimate surface water quality parameters has been evaluated, and in most of them, acceptable results have been obtained indicating the ability of this technology in the issue as mentioned above. Among these studies, we can mention laili et al. (2015), in which in a small section of Indonesian waters, have figured out a new regression algorithm between Landsat 8 and groundwater quality parameters. Toming et al. (2016) in a study using satellite images of Sentinel-2 on the water quality of the lakes in Estonia, could find a good correlation between the satellite band proportions and ground. The purpose of this research is to establish a relation between satellite images of Sentinel-2 A and two quality water parameters with a suitable model along the Karun and Dez River. For this purpose, firstly suitable spectral indices were extracted from them by applying the necessary processing on satellite images. In the next step, optimal relationships between extracted indices and water quality parameters are established using different models. Finally, using models with higher accuracy in terms of modeling, the dispersion map of each parameter in the length of the Karun River is provided.The purpose of this research is to establish a relation between satellite images of Sentinel-2 A and 2 quality water parameters with a suitable model along the Karun and Dez River. For this purpose, firstly suitable spectral indices were extracted from them by applying the necessary processing on satellite images. In the next step, optimal relationships between extracted indices and water quality parameters are established using different models. Finally, using models with higher accuracy in terms of modeling, the dispersion map of each parameter in the length of the Karun River is provided. 

This study presented in eight steps as below: Step 1: Preparation of ground data and satellite imagery:The ground data used in this study is the measured data at the water quality sampling stations. The data included information on these quality parameters that were used from 2015 to early 2017 in ten stations. Step 2: Recording the value of the reflection bands at the ground measurement stations:In order to implement this research, satellite images of sentinel-2 and groundwater quality parameters were collected and measured at the same time from the study area. In this step, the values of measured water quality parameters were also sorted by date and sampling stations were prepared in separate files. Step 3: Analyze the initial sensitivity and determine the bands that have a stronger connection with each water quality parameter  

Table 1: result of sensitivity analysis for sentinel-2 bands TDS Turbidity EC pH Hco3 So4 Cl Na K Mg Ca Parameter Type Band Number 0.376 0.472 0.296 0.384 0.493 0.219 0.338 0.279 0.312 0.217 0.294 B2 0.379 0.303 0.325 0.307 0.238 0.239 0.268 0.238 0.179 0.291 0.217 B3 0.352 0.237 0.283 0.278 0.260 0.232 0.225 0.269 0.165 0.196 0.269 B4 0.346 0.332 0.274 0.428 0.315 0.214 0.256 0.294 0.256 0.275 0.313 B5 0.401 0.208 0.248 0.322 0.294 0.278 0.253 0.249 0.210 0.268 0.239 B6 0.403 0.257 0.227 0.299 0.273 0.281 0.258 0.256 0.203 0.283 0.210 B7 0.263 0.285 0.301 0.346 0.198 0.245 0.231 0.227 0.184 0.209 0.224 B8 0.422 0.306 0.316 0.309 0.241 0.275 0.251 0.244 0.195 0.299 0.212 B8a 0.249 0.205 0.267 0.325 0.238 0.273 0.233 0.287 0.205 0.209 0.158 B11 0.391 0.265 0.214 0.310 0.282 0.293 0.254 0.247 0.270 0.244 0.178 B12

  Step 4: Calculating spectral indices and selecting spectral indicators with higher correlation Step 5: Secondary Sensitivity Analysis and Selection of Spectral Indicators with Stronger Connections   In the next step, by applying the sensitivity analysis method, the relationship between each spectral indicator and water quality parameters was calculated (Table 2).

  Table 2. Result of sensitivity analysis for spectral indicator TDS Turbidity EC pH So4 Hco3 Cl Na K Mg Ca Parameter Type   Spectral Indexes 0.455 0.580 0.470 0.407 0.534 0.260 0.482 0.535 0.364 0.511 0.366 Single bans reflectance 0.465 0.659 0.563 0.516 0.599 0.501 0689 0.688 0.670 0.532 0.666 ( 14BmaxBmin)"> 0.436 0.740 0.452 0.633 0.562 0.681 0.701 0.598 0.600 0.485 0.677 ( 14BminBmax)"> 0.396 0.702 0.438 0.720 0.527 0.581 0.758 0.669 0.656 0.506 0.740 ( 14Bmax-BminBmax+Bmin)">   Step 6: Normalization of data Step 7: Modeling the relationship between satellite images and groundwater quality parameters:In order to model the relationship between satellite images and groundwater quality parameters, and based on the results of previous steps, the normalized values derived from the calculation of spectral indices were determined as inputs and water quality parameters were determined as outputs of ANN and ANFIS models. Step 8: Providing water dispersion map for water quality parameters:At this step, the modeling process was repeated with the transformation of ANN and ANFIS models until each model was accurately mapped the relationship between water quality parameters. 

Table 3 shows the evaluation result of used model in this study.   Table 3. Evaluation result of ANN and ANFIS model for water quality parameters. Hco3 So4 Cl Na K Mg Ca WQPT ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN Error Type 0.497 0.315 0.0871 0.691 0.266 0.263 0.229 0.264 0.136 0.0709 0.127 0.397 0.120 0.279 RE 0.164 0.131 0.0587 0.311 0.0959 0.0748 0.102 0.079 0.126 0.0605 0.077 0.157 0.115 0.194 RMSE   Figures 1– 4 show the concentration map of TDS and turbidity parameters studied in this research in Karun River in Dez and Karkheh dam and the Karun River from Malasani section to the Farsiat station.                                  (a)                               (b) Figure 1: Concentration map of TDS parameter in a) Karkheh and b) Dez dam.   Figure 2. Concentration map of TDS parameter Karun River.     (a)   (b) Figure 3. Concentration map of turbidity parameter in A) Karkheh and b) Dez dam.   Figure

map of turbidity parameter Karun River.   4- Conclusion In this study, two models of ANN and ANFIS computational intelligence models were used to model the relationship between satellite images of Sentinel-2 and two quality parameters of water along the Karun River. The results of this study indicate the high level of remote sensing ability to monitor water quality, similar to other studies; as this is well understood in previous researches, remote sensing technology can be widely used to monitor other surface water resources of Khuzestan province.   References Bagherian.Marzouani.M., Akhoundali.A.M., Moazed.H., Jaafarzadeh.N., Ahadian.J., Hasoonizadeh.H., 2014, Evaluation of Karun River Water Quality Scenaros Using Simulation Model Results, International Journal of Advanced Biological and Biomedical Research, Volume 2, Issue 2, 339-358. Bonansea.M., Claudia Rodriguez.M., Pinotti.L., Ferrero.S., 2015, Using multi-temporal Landsat imagery and linear Models for assessing water quality parameters in Rio Tercero reservoir (Argentina), Journal of Remote Sensing of Environment, 158, 28–41. Laili, N., Arafah, F., Jaelani, L., Subehi, L., Pamungkas, A., Koenhardono, E., & Sulisetyono, A. 2015. Development of water quality parameter retrieval algorithms for estimating total suspended solids and chlorophyll-a concentration using landsat-8 imagery at poteran island water. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(2), 55. Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, P., Nõges T., 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery, Remote Sensing journal, 8, 640.