Agricultural Meteorology
Nazila Shamloo; Mohammad Taghi Sattari; Khalil Valizadeh Kamran; Halit Apaydin
Abstract
Introduction
Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information ...
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Introduction
Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.
Materials and Methods
In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.
Results and Discussion
This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.
Conclusion
In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.
Soil science
A. Sarabchi; H. Rezaei; F. Shahbazi
Abstract
Introduction
High-resolution satellite imagery data is widely utilized for Land Use/Land Cover (LULC) mapping. Analyzing the patterns of LULC and the data derived from changes in land use caters to the increasing societal demands, improving convenience, and fostering a deeper comprehension of the interaction ...
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Introduction
High-resolution satellite imagery data is widely utilized for Land Use/Land Cover (LULC) mapping. Analyzing the patterns of LULC and the data derived from changes in land use caters to the increasing societal demands, improving convenience, and fostering a deeper comprehension of the interaction between human activities and environmental factors. Although numerous studies have focused on remote sensing for LULC mapping, there is a pressing need to improve the quality of LULC maps to achieve sustainable land management, especially in light of recent advancements made. This study was carried out in an area covering approximately 8000 hectares, characterized by diverse conditions in LULC, geomorphology and pedology. The objective was to investigate the potential for achieving maximum differentiation and accurate mapping of land features related to LULC. Additionally, the study assessed the impact of various spectral indices on enhancing the results from the classification of Landsat 8 imagery, while also evaluating the efficacy of support vector machine (SVM) and maximum likelihood algorithms in producing maps with satisfactory accuracy and precision.
Materials and Methods
As an initial step, LULC features were identified through fieldwork, and their geographic coordinates were recorded using GPS. These features included various types of LULC, soil surface characteristics, and landform types. Following the fieldwork, 12 types of LULC units were identified. Subsequently, the LULC pattern in the study area was classified using the RGB+NIR+SWIR1 bands of Landsat 8, employing both SVM and maximum likelihood classifiers. To assess the impact of various spectral indices on improving the accuracy of the LULC maps, a set of vegetation indices (NDVI, SAVI, LAI, EVI, and EVI2), bare soil indices (BSI, BSI3, MNDSI, NBLI, DBSI, and MBI), and integrated indices (TLIVI, ATLIVI, and LST), and digital elevation model of study area were successively incorporated into the classification algorithms. Finally, the outcomes from the two classification algorithms were compared, taking into account the influence of the applied indexes. The classification process continued with the selected classifier and indices until reaching the maximum overall accuracy and kappa coefficient.
Results and Discussion
Field observations revealed that the study area could be categorized into 12 primary LULC units, including irrigated farms, flow farming, dry farming, traditional gardens (with no evident order observed among planted trees), modern gardens (featuring regular rows where soil reflectance is visible between tree rows), grasslands, degraded grasslands, highland pastures (covered by Astragalus spp., dominantly), lowland pastures (covered by halophyte plants), salt domes (with no or very poor vegetation), outwash areas (River channel with many waterways), and resistant areas. The results of image classification indicated that the performance of the SVM algorithm across different band combinations is superior to that of the maximum likelihood method. Using SVM resulted in an increase in overall accuracy and Kappa coefficient by 3-8% and 0.03-0.08, respectively. For the map generated using RGB+NIR+SWIR1 bands and employing SVM, overall accuracy and Kappa coefficient were determined to be 76.6% and 0.72, respectively. Among the vegetation indices used in the SVM algorithm, LAI had the most significant impact, increasing the classification accuracy by 2.64%. Among the soil indices, BSI and MBI indices demonstrated the best performance; with BSI increasing the classification accuracy by 1.95% and MBI by 1.64%. Among the integrated indices, LST and ALTIVI enhanced the classification accuracy by 2.75% and 2.35%, respectively. It should be noted that the inclusion of the digital elevation model did not significantly improve the classification accuracy when using the support vector machine algorithm; in fact, it led to a decrease in accuracy when applied to the maximum likelihood classification. The probable reason for this issue is the different nature of DEM data compared to the other input data, as well as the limitations of parametric statistical approaches to effectively integrating data from diverse sources. Finally, the classification process was executed using the three visible bands, NIR, and SWIR1, in conjunction with selected indices (LAI, BSI, MBI, LST, and ALTIVI). Results indicated that using these spectral indices significantly improved classification accuracy, particularly for the DF, DGL, MG, O, and IF land cover/use classes. The calculated accuracies for these classes increased by 11.62%, 18.57%, 20.06%, 29.39%, and 33.19% respectively. Consequently, the accuracy of the classification and the Kappa coefficient (using support vector machine algorithm) increased to 85.24% and 0.82, respectively.
Conclusion
In this research, we aimed to accurately map various land use/land covers by utilizing Landsat 8 imagery and incorporating three group of spectral indexes. Despite spectral interferences and overlaps among various phenomena related to LULC, the utilization of different spectral indices resulted in significant differentiation among LULC classes. Finally, considering the limitations of modelling in ENVI software, it is recommended to investigate the effectiveness of other models for classification in more specialized software, such as R.
Irrigation
Z. Bigdeli; A. Majnooni-Heris; R. Delearhasannia; S. Karimi
Abstract
Introduction
Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface ...
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Introduction
Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area.
Materials and Methods
The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40´ to 46° and 25´and latitudes from 37° and 15´ to 37° and 55´ north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination.
Calculating average rainfall through the Thiessen Polygons method
Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to:
(1)
Random Forest Algorithm
Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks.
Support Vector Machine (SVM) algorithm
Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression.
Evaluation Criteria
To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R2) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria:
(2)
(3)
(4)
(5)
Results and Discussion
Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values.
As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively.
Conclusion
The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend.
M. Abiyat; M. Abiyat; M. Abiyat
Abstract
Introduction Agriculture is the essential sector for promoting food security. Crop area estimation (CAE) can meet the requirements of the crop monitoring plan. The organizing basis of the cultivation pattern is recognizing the types of crops and examining the condition of their crop area. Shush ...
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Introduction Agriculture is the essential sector for promoting food security. Crop area estimation (CAE) can meet the requirements of the crop monitoring plan. The organizing basis of the cultivation pattern is recognizing the types of crops and examining the condition of their crop area. Shush county in Khuzestan Province has 300,000 hectares of the crop area. It is one of the agricultural hubs of Iran because it has a record annual production of more than two million tons of strategic crops such as wheat, sugar beet, and corn. CAE affects the amount of net production and shortage or surplus of produce for market steadiness. Traditional approaches for CAE are time-consuming and costly and are not widely enforceable. Remote sensing (RS) data provide good information for decision-makers by determining the crop type and the crop area. RS data has made it possible to avoid continuous reference to agricultural lands with less time and cost than another usual method and accurate CAE. Also, the use of multi-time images during the growing season of agricultural products allows the use of spectral curves when related to the crop calendar of each crop. This spectral curve is almost separate for each product and increases the ability to distinguish between products. Therefore, multi-temporal images support segregation based on multispectral images of products. The current study follows a speedy method with appropriate accuracy established on satellite image classification algorithms and spectral indices to identify and separate crops with RS data in Shush County.Materials and Methods Landsat-8 data with path/row coordinates 166/38 extracted from the USGS website were used to identify and separate the cultivated lands of the region. The reason for choosing Landsat images is the relatively suitable temporal and spatial resolution, availability, and the appropriate time distribution with the product growth period. The Landsat 8 carries 2-sensors, OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor). The OLI sensor with a spatial resolution of 30 meters has 8-bands in the visible spectrum, near-infrared (NIR), short-wavelength infrared (SWIR), and a panchromatic band with a spatial resolution of 15 meters. The TIRS sensor can record thermal infrared radiation with a spatial resolution of 100 meters with the help of 2-bands in atmospheric windows of 10.6 to 11.2 micrometers for band 10 and 11.5 to 12.5 micrometers for band 11. This research used bands 1-7 of the Landsat-8 OLI sensor with a spatial resolution of 30 meters after the initial corrections of satellite images. The spectral similarity between the region's dominant crops has made it impossible to select a single image to differentiate and extract the cultivation pattern. Wheat and barley have a high spectral similarity. The peak of the greenness of these products is in the first four months of the year, which has high NDVI values at this time. Therefore, choosing a good time to separate the crops was feasible by referring to the Khuzestan Organization Agriculture-Jihad (KOAJ) and receiving the regional crops calendar in 2018-19. Then, the low-level cloud cover images on April 24, June 27, and August 30, 2019, were selected for classification based on the crop calendar. Planting, harvesting, maximum greenness, and ripening information of the dominant crops in the area were pivotal in obtaining image dates. In dates selected related to the images were considered planting, harvesting, maximum greenery, and ripening information of the region's dominant crops.Results and Discussion According to the results, from total crop area in Shush county (163313.7 hectares) is allocated about 103513.2 hectares (63.4% of the county's crop area) to the ANN, about 102875.1 hectares (63.0% of the county's crop area) to the SVM, and about 102,277.3 hectares (62.6% of the county's crop area) to the NDVI, which in comparison with the KOAJ statistics, has an error of 0.11, 6.2 and 1.8%, respectively.This difference is the similarity of the reflective spectrum in some places, which affects the separability and recognition of phenomena and increases the error in estimating the area under cultivation of different crops. The highest and lowest errors in estimating the area under cultivation in the artificial neural network method were in barley and rice crops, respectively, in the support vector machine method were in wheat and rice crops, respectively, and in NDVI index were in wheat and barley crops, respectively. The difference between the cropped area obtained from classification methods and NDVI index with cropped area statistics of Agricultural-Jihad Organization may be due to the following: First, the cultivation history of different has caused problems such as reflections of diverse agricultural lands in one image. Second, the agricultural lands in this area are small. Most of them are under one hectare. Also, the crops in this area are diverse. Third, the smallest region that the image used in the present study can distinguish is about 900 square meters, which is a large number for the agricultural lands of the study area and causes errors.Conclusion The study results showed that the support vector machine method had the lowest error in CAE than the artificial neural network method, which indicates the higher accuracy of the support vector method in identifying and separating crops in the region. Comparing the area obtained from the NDVI index with the statistics of the Agricultural-Jihad Organization of Khuzestan province and evaluating the accuracy of this method indicated the higher efficiency of spectral indices in CAE for the region compared to classification methods. The NDVI index minimizes the error values of the results due to having a threshold and better identification of vegetation density. Therefore, based on the accuracy assessment results and comparing the cropped area with the KOAJ statistics, the utilization of the NDVI index provides the best CAE in the region.
Mohammad Ali Mahmoodi; Sohaila Momeni; Masoud Davari
Abstract
Introduction: Land use and Land cover (LULC) information has been identified as one of the crucial data components for a range of applications including global change studies, urban planning, agricultural crop characterization, and forest ecosystem classification. The derivation of such information increasingly ...
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Introduction: Land use and Land cover (LULC) information has been identified as one of the crucial data components for a range of applications including global change studies, urban planning, agricultural crop characterization, and forest ecosystem classification. The derivation of such information increasingly relies on remote sensing technology due to its ability to acquire valuable spatiotemporal information on LULC. One of the major approaches to deriving LULC information from remotely sensed images is classification. Numerous image classification algorithms exist. Among the most popular are the maximum likelihood classifier (MLC), artificial neural network (ANN) classifiers and decision tree (DT) classifiers. Conventional parametric method like MLC is based on statistical theory and assumes a multivariate normal distribution for each class. In case of data that has non-normal distribution (which is common with LULC data), the parametric classifiers may fail since the inability to resolve interclass confusion. This inability is the major limitation of parametric classifiers. Nonparametric classifiers like ANNs and DTs, which do not rely on any assumptions for the class distributions of data, could overcome the aforementioned limitations of parametric classifiers. The support vector machines (SVMs), a nonparametric classifier, that has recently been used in numerous applications in image processing, represents a group of theoretically superior machine learning algorithms. The SVM employs optimization algorithms to locate the optimal boundaries between classes. It was found competitive with the best available classification methods, including ANN and DT classifiers. The classification accuracy of SVMs is based upon the choice of the classification strategy and kernel function. The objective of this study was to investigate the sensitivity of SVM architecture including classification strategy and kernel types to identify LULC information from Landsat Enhanced Thematic Mapper (ETM) remote sensing data in Gavshan dam watershed in west of Iran.
Materials and Methods: SVMs were used to classify orthocorrected Landsat ETM images of May, 2016. Image pre-processing such as atmospheric correction were conducted before utilization. Three classification strategies (One versus one, one versus all and ordinal) and three types of kernels (linear, polynomial and radial basis function) were used for the SVM classification. A total of 18 different models were developed and implemented for sensitivity analysis of SVM architecture. A two-layer feed-forward Perceptron network classifier with sigmoid hidden and softmax output neurons was also used for comparison. The network was trained using scaled conjugate gradient backpropagation algorithm. A total of 1320 ground control points were collected to train, validate and test the SVM and ANN models. Ground truth locations on each image were identified using the GPS coordinates for extracting spectral reflectance data of seven bands (Bands 1-7) of Landsat ETM images. The LULC class of each point was identified using land survey or Google earth images. The identified LULC classes were agriculture, buffer forests, orchard, ranges brush, range grasses, urban areas, roads and water.
Results and Discussion: The results suggest that the choice of classification strategy and kernel types play an important role on SVMs classification accuracy. Statistical evaluation of the SVM models against the ground control points showed that the one versus one classification strategy had the highest accuracy than the two other ones for any kernel function type and the polynomial kernel function had the highest accuracy than the two other kernels for any classification strategy. The SVM model with polynomial (n=3) kernel and one versus one classification strategy outperformed all SVMs models and gave the highest overall classification accuracy of 78.5 and Kappa coefficient of 68.5. The McNemar’s test clearly showed significant improvement of the best SVM model in comparison to the ANN model (P<0.001). Also, the user accuracy and producer accuracy achieved by best SVM model were higher than ANN model for all LULC classes. In both approaches water and agriculture categories have high accuracy while roads have low accuracy. The resulting LULC map indicated that most parts of the studied area (52.8%) have been assigned to the agriculture. The ranges brush and range grasses categories cover 12.5% and 26.8% of the watershed, respectively. Only about 2.7% of the watershed have been covered with trees.
Conclusions: This study suggests that the SVMs approach based on Landsat ETM bands may provide reliable and accurate LULC information even better that best ANN approaches. However, choice of classification strategy and kernel types play an important role on SVMs classification accuracy. Best model of polynomial kernel and one versus one classification strategy outperformed all SVMs and ANN models and gave the highest classification accuracy.
F. Ahmadi; F. Radmanesh; Rasoul Mirabbasi
Abstract
Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at ...
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Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.
