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With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers.
Laleh Ghayour; Aminreza Neshat; Sina Paryani; Himan Shahabi; Ataollah Shirzadi; Wei Chen; Nadhir Al-Ansari; Marten Geertsema; Mehdi Pourmehdi Amiri; Mehdi Gholamnia; Jie Dou; Anuar Ahmad. Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sensing 2021, 13, 1349 .
AMA StyleLaleh Ghayour, Aminreza Neshat, Sina Paryani, Himan Shahabi, Ataollah Shirzadi, Wei Chen, Nadhir Al-Ansari, Marten Geertsema, Mehdi Pourmehdi Amiri, Mehdi Gholamnia, Jie Dou, Anuar Ahmad. Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sensing. 2021; 13 (7):1349.
Chicago/Turabian StyleLaleh Ghayour; Aminreza Neshat; Sina Paryani; Himan Shahabi; Ataollah Shirzadi; Wei Chen; Nadhir Al-Ansari; Marten Geertsema; Mehdi Pourmehdi Amiri; Mehdi Gholamnia; Jie Dou; Anuar Ahmad. 2021. "Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms." Remote Sensing 13, no. 7: 1349.
Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 m and UAV Drone data from 300 and 500 m flying height. RAW UAV images acquired from 500 m flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 m flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 m flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 m to ±0.11 m. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.
Muhammad Chaudhry; Anuar Ahmad; Qudsia Gulzar; Muhammad Farid; Himan Shahabi; Nadhir Al-Ansari. Assessment of DSM Based on Radiometric Transformation of UAV Data. Sensors 2021, 21, 1649 .
AMA StyleMuhammad Chaudhry, Anuar Ahmad, Qudsia Gulzar, Muhammad Farid, Himan Shahabi, Nadhir Al-Ansari. Assessment of DSM Based on Radiometric Transformation of UAV Data. Sensors. 2021; 21 (5):1649.
Chicago/Turabian StyleMuhammad Chaudhry; Anuar Ahmad; Qudsia Gulzar; Muhammad Farid; Himan Shahabi; Nadhir Al-Ansari. 2021. "Assessment of DSM Based on Radiometric Transformation of UAV Data." Sensors 21, no. 5: 1649.
Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.
Muhammad Hamid Chaudhry; Anuar Ahmad; Qudsia Gulzar. Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling. ISPRS International Journal of Geo-Information 2020, 9, 656 .
AMA StyleMuhammad Hamid Chaudhry, Anuar Ahmad, Qudsia Gulzar. Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling. ISPRS International Journal of Geo-Information. 2020; 9 (11):656.
Chicago/Turabian StyleMuhammad Hamid Chaudhry; Anuar Ahmad; Qudsia Gulzar. 2020. "Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling." ISPRS International Journal of Geo-Information 9, no. 11: 656.
Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.
Himan Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing 2020, 12, 266 .
AMA StyleHiman Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, Ebrahim Omidvar, Nadhir Al-Ansari, John J. Clague, Marten Geertsema, Khabat Khosravi, Ata Amini, Sepideh Bahrami, Omid Rahmati, Kyoumars Habibi, Ayub Mohammadi, Hoang Nguyen, Assefa M. Melesse, Baharin Bin Ahmad, Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing. 2020; 12 (2):266.
Chicago/Turabian StyleHiman Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. 2020. "Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier." Remote Sensing 12, no. 2: 266.
The demand of aerial photogrammetry has increased recently especially after the development of unmanned aerial vehicle system. This study explores the use of different UAV systems which comprised of conventional UAV, UAV RTK and UAV Lidar systems. This study also comprises of three experiments. The first experiment involved the mapping of Lingkaran Ilmu, UTM by using fixed wing Ebee UAV with 20megapixel digital camera. This first experiment used conventional UAV. The second experiment involved the fixed wing Ebee UAV equipped with real time kinematic (RTK) system on-board for mapping the study area. The last experiment is the used of octacopter UAV equipped with Riegl Lidar system for mapping the study area. The study area for all experiments is located in Lingkaran Ilmu of main campus Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia. Ebee UAV and Ebee RTK UAV are flown in autonomous mode at 200 meters altitude. Both systems are used to capture high resolution aerial photo of the study area. Riegl UAV Lidar system is flown at 100 meter altitude for capture high resolution and point cloud data. GPS rapid static method was used for establishing ground control points (GCP) and check point (CP) in the study area. Three different GCP configuration was applied in geometry correction. Meanwhile, CPs is used for accuracy assessment where RMSE equation was employed. The 15CGP configuration produce more accurate result compared to another. Where, the planimetric RMSE values of Ebee UAV, Ebee RTK UAV and Riegl UAV Lidar are 0.21 m, 0.09 m and 0.15 m respectively. For height RMSE values for Ebbe, Ebee RTK and Octacopter Lidar are 0.34 m, 0.13 m and 0.07 m respectively. In conclusion, Ebee RTK UAV is identified as a system that can produce an accurate digital orthophoto compared to other systems while Riegl UAV Lidar system can produce highest accurate DEM and DTM compared to other systems in 15GCP configuration.
M. H. M. Room; A. Ahmad; M. A. Rosly. ASSESSMENT OF DIFFERENT UNMANNED AERIAL VEHICLE SYSTEM FOR PRODUCTION OF PHOTOGRAMMERTY PRODUCTS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W16, 549 -554.
AMA StyleM. H. M. Room, A. Ahmad, M. A. Rosly. ASSESSMENT OF DIFFERENT UNMANNED AERIAL VEHICLE SYSTEM FOR PRODUCTION OF PHOTOGRAMMERTY PRODUCTS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W16 ():549-554.
Chicago/Turabian StyleM. H. M. Room; A. Ahmad; M. A. Rosly. 2019. "ASSESSMENT OF DIFFERENT UNMANNED AERIAL VEHICLE SYSTEM FOR PRODUCTION OF PHOTOGRAMMERTY PRODUCTS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16, no. : 549-554.
Evaluating water level changes at intertidal zones is complicated because of dynamic tidal inundation. However, water level changes during different tidal phases could be evaluated using a digital surface model (DSM) captured by unmanned aerial vehicle (UAV) with higher vertical accuracy provided by a Global Navigation Satellite System (GNSS). Image acquisition using a multirotor UAV and vertical data collection from GNSS survey were conducted at Kilim River, Langkawi Island, Kedah, Malaysia during two different tidal phases, at high and low tides. Using the Structure from Motion (SFM) algorithm, a DSM and orthomosaics were produced as the main sources of data analysis. GNSS provided horizontal and vertical geo-referencing for both the DSM and orthomosaics during post-processing after field observation at the study area. The DSM vertical accuracy against the tidal data from a tide gauge was about 12.6 cm (0.126 m) for high tide and 34.5 cm (0.345 m) for low tide. Hence, the vertical accuracy of the DSM height is still within a tolerance of ±0.5 m (with GNSS positioning data). These results open new opportunities to explore more validation methods for water level changes using various aerial platforms besides Light Detection and Ranging (LiDAR) and tidal data in the future.
Norhafizi Mohamad; Mohd Faisal Abdul Khanan; Anuar Ahmad; Ami Hassan Md Din; Himan Shahabi. Evaluating Water Level Changes at Different Tidal Phases Using UAV Photogrammetry and GNSS Vertical Data. Sensors 2019, 19, 3778 .
AMA StyleNorhafizi Mohamad, Mohd Faisal Abdul Khanan, Anuar Ahmad, Ami Hassan Md Din, Himan Shahabi. Evaluating Water Level Changes at Different Tidal Phases Using UAV Photogrammetry and GNSS Vertical Data. Sensors. 2019; 19 (17):3778.
Chicago/Turabian StyleNorhafizi Mohamad; Mohd Faisal Abdul Khanan; Anuar Ahmad; Ami Hassan Md Din; Himan Shahabi. 2019. "Evaluating Water Level Changes at Different Tidal Phases Using UAV Photogrammetry and GNSS Vertical Data." Sensors 19, no. 17: 3778.
This study proposes a site location assessment model for citrus cropland using multi-criteria evaluation (MCE) and the combination of a set of factors for suitability mapping and delineating the suitable areas for citrus production in Ramsar, Iran. It defines an incorporated method for the suitability mapping of the most appropriate sites for citrus cultivars with an emphasis on the multi-criteria decision analysis (MCDA) process. The combination of geographic information system (GIS) and a modified version of the analytic hierarchy process (AHP) based on the ordered weighted averaging (OWA) technique is also emphasized. The OWA is based on two principles, namely: the weights of relative criterion significance and the order weights. Therefore, the participatory technique was employed to outline the set of standards and the important criterion. The results derived from the GIS–OWA technique indicate that the cultivation of citrus is feasible only in limited areas, which make up 6.7% of the total area near the Caspian Sea. This investigation has shown that the GIS–OWA model can be integrated into MCDA to select the optimal site for citrus production. The present research highlights how multi-criteria in GIS can play a considerable role in decision making for evaluating the suitability of selected sites for citrus production.
Hasan Zabihi; Mohsen Alizadeh; Philip Kibet Langat; Mohammadreza Karami; Himan Shahabi; Anuar Ahmad; Mohamad Nor Said; Saro Lee. GIS Multi-Criteria Analysis by Ordered Weighted Averaging (OWA): Toward an Integrated Citrus Management Strategy. Sustainability 2019, 11, 1009 .
AMA StyleHasan Zabihi, Mohsen Alizadeh, Philip Kibet Langat, Mohammadreza Karami, Himan Shahabi, Anuar Ahmad, Mohamad Nor Said, Saro Lee. GIS Multi-Criteria Analysis by Ordered Weighted Averaging (OWA): Toward an Integrated Citrus Management Strategy. Sustainability. 2019; 11 (4):1009.
Chicago/Turabian StyleHasan Zabihi; Mohsen Alizadeh; Philip Kibet Langat; Mohammadreza Karami; Himan Shahabi; Anuar Ahmad; Mohamad Nor Said; Saro Lee. 2019. "GIS Multi-Criteria Analysis by Ordered Weighted Averaging (OWA): Toward an Integrated Citrus Management Strategy." Sustainability 11, no. 4: 1009.
The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
Ataollah Shirzadi; Karim Soliamani; Mahmood Habibnejhad; Ataollah Kavian; Kamran Chapi; Himan Shahabi; Wei Chen; Khabat Khosravi; Binh Thai Pham; Biswajeet Pradhan; Anuar Ahmad; Baharin Bin Ahmad; Dieu Tien Bui. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors 2018, 18, 3777 .
AMA StyleAtaollah Shirzadi, Karim Soliamani, Mahmood Habibnejhad, Ataollah Kavian, Kamran Chapi, Himan Shahabi, Wei Chen, Khabat Khosravi, Binh Thai Pham, Biswajeet Pradhan, Anuar Ahmad, Baharin Bin Ahmad, Dieu Tien Bui. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors. 2018; 18 (11):3777.
Chicago/Turabian StyleAtaollah Shirzadi; Karim Soliamani; Mahmood Habibnejhad; Ataollah Kavian; Kamran Chapi; Himan Shahabi; Wei Chen; Khabat Khosravi; Binh Thai Pham; Biswajeet Pradhan; Anuar Ahmad; Baharin Bin Ahmad; Dieu Tien Bui. 2018. "Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping." Sensors 18, no. 11: 3777.
This study aims to compare the accuracies of ASTER DEM, ASTER GDEM, and SRTM DEM for the area of Universiti Teknologi Malaysia (UTM) and surrounding. In doing so, a number of Ground Control Points (GCPs) were collected using GPS technology and used to generate an absolute DEM using the ASTER stereo imagery. Moreover, two well-known DEMs including ASTER GDEM and SRTM DEM were obtained for the same area with ASTER image. Subsequently, several high accuracy ground-truth points were established around UTM using dual frequency GPS and used to assess the accuracies of the obtained DEMs. The results indicate that an elevation Root Mean Square Error (RMSE) of ±14.86m is achieved for the generated ASTER DEM, which is less than the 15m pixel size of ASTER image. The results further show that the elevation RMSEs of the ASTER GDEM and SRTM DEM are respectively ±4.52m and ±4.14m for the study area. The results illustrate although the resolution of SRTM DEM is much lower than ASTER GDEM, it could provide higher elevation accuracy. Finally, although the accuracy of the ASTER DEM in this study is not high in comparison with the accuracies of ASTER GDEM and SRTM DEM, based on the selected number of check points and resolution of ASTER image, it could be useful for various geoinformation applications.
Komeil Rokni; Anuar Ahmad; Sharifeh Hazini. COMPARATIVE ANALYSIS OF ASTER DEM, ASTER GDEM, AND SRTM DEM BASED ON GROUND-TRUTH GPS DATA. Jurnal Teknologi 2015, 76, 1 .
AMA StyleKomeil Rokni, Anuar Ahmad, Sharifeh Hazini. COMPARATIVE ANALYSIS OF ASTER DEM, ASTER GDEM, AND SRTM DEM BASED ON GROUND-TRUTH GPS DATA. Jurnal Teknologi. 2015; 76 (1):1.
Chicago/Turabian StyleKomeil Rokni; Anuar Ahmad; Sharifeh Hazini. 2015. "COMPARATIVE ANALYSIS OF ASTER DEM, ASTER GDEM, AND SRTM DEM BASED ON GROUND-TRUTH GPS DATA." Jurnal Teknologi 76, no. 1: 1.
Zahrul Umar; Biswajeet Pradhan; Anuar Ahmad; Mustafa Neamah Jebur; Mahyat Shafapour Tehrany. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. CATENA 2014, 118, 124 -135.
AMA StyleZahrul Umar, Biswajeet Pradhan, Anuar Ahmad, Mustafa Neamah Jebur, Mahyat Shafapour Tehrany. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. CATENA. 2014; 118 ():124-135.
Chicago/Turabian StyleZahrul Umar; Biswajeet Pradhan; Anuar Ahmad; Mustafa Neamah Jebur; Mahyat Shafapour Tehrany. 2014. "Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia." CATENA 118, no. : 124-135.
Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWIwas found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously.
Komeil Rokni; Anuar Ahmad; Ali Selamat; Sharifeh Hazini. Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sensing 2014, 6, 4173 -4189.
AMA StyleKomeil Rokni, Anuar Ahmad, Ali Selamat, Sharifeh Hazini. Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sensing. 2014; 6 (5):4173-4189.
Chicago/Turabian StyleKomeil Rokni; Anuar Ahmad; Ali Selamat; Sharifeh Hazini. 2014. "Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery." Remote Sensing 6, no. 5: 4173-4189.