Sinan Jasim Hadi

Modelling LULC for the period 2010-2030 using GIS and Remote sensing: a case study of Tikrit, Iraq

This study extends upon the results of [1] to include the modeling of Land use/ Land cover (LULC). This study looks at the changes that occurred from 2010 to 2030 in Tikrit district, Iraq by predicting LULC for the year target 2030 by using the classified images for two points of time (2000 – 2010) as a foundation for the modeling process. The projected map, in comparison with 2010 LULC map, shows a significant decrease in vegetation area (45.11 km2) which must be regulated in order to maintain a green environment, and increase in the urban area (58.42 km2) which should be monitored to have sustainable development and control the eco-environment degradation. Also, in this study, it is shown clearly that the use of Geographic Information System (GIS) and remote sensing (specially IDRISI software) in modeling LULC is a suitable approach to understand the future pattern.

Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods

Modelling streamflow is essential for activities, such as flood control, drought mitigation, and water resources utilization and management. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM) are techniques that are frequently used in hydrology to specifically model streamflow. This study compares the accuracy of ANN, ANFIS, and SVM in forecasting the daily streamflow with the traditional approach known as autoregressive (AR) model for basins with different physical characteristics. The accuracies of the models are compared for three basins, that is, 1801, 1805, and 1822, at the Seyhan River Basin in Turkey. The comparison was performed by using coefficient of efficiency, index of agreement, and root-mean-square error. Results indicate that ANN and ANFIS are more accurate than AR and SVM for all the basins. ANN and ANFIS perform similarly, while ANN outperformed ANFIS. Among the models used, the ANN demonstrates the highest performance in forecasting the peak flood values. This study also finds that physical characteristics, such as small area, high slope, and high elevation variation, and streamflow variance deteriorate the accuracy of the methods.

Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study

This study investigates the use of wavelet transformation (WT) as preprocessing tool in data-driven models (DDMs) for forecasting streamflow 7xa0days ahead. WT used are Continuous wavelet transformation (CWT), discrete wavelet transformation (DWT), and a new proposed combination of CWT and DWT, namely discrete continuous wavelet transformation (DCWT). In addition to these three different WTs, the single DDMs were used also to create four different schematic layouts. The DDMs applied were artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM). The lagged rainfall, temperature, and streamflow were incorporated as inputs into the WT-DDMs. It was found that CWT improved the forecasting accuracy of models which only included the rainfall and temperature but not the streamflow. Moreover, DWT improved the performance dramatically for the models with streamflow. Notably, DWT layout outperformed CWT layout in general while CWT layouts resulted in higher improvement to the models with rainfall and temperature only. The proposed DCWT in which CWT applied on the rainfall and temperature variables and DWT applied on the streamflow improved the forecasting ability in several models combinations when ANN was applied. Nevertheless, improvement in the forecasting accuracy was deteriorated in those with SVM while no improvement was observed with ANFIS. ANN outperformed both ANFIS and SVM while ANFIS performed better than SVM.

Trend of normalized difference vegetation index (NDVI) over Turkey

Ecosystem productivity, biome distribution, and forest carbon stocks are likely to be changed by the climate change. These ecosystems changes can be identified using Satellite based normalized difference vegetation index (NDVI). In this study, global inventory modeling and mapping studies (GIMMS) NDVI data acquired by the advanced very highresolution radiometer (AVHRR) was used for analyzing the trend over Turkey for the period 1982-2015. The acquired data has bi-monthly nature, and the maximum value of composite method was used for finding monthly NVDI. The obtained NDVI was then clipped to the study area, and then the trend was estimated using Annual aggregated time series (AAT) and seasonal adjusted time series (SAT) methods. In AAT method the annual averages were calculated and then the trend was estimated. In SAT, the seasonal component removed from the time series and the seasonal adjusted time series used for estimating the trend. The gradient latitudinal and longitudinal trend was also implemented to investigate the spatial trend. The gradient was calculated as the trend of the blocks of a specific latitude of longitude over the whole Turkey to have better interpretation of the spatial trend from south to north and east to west. The results showed that throughout Turkey the NDVI has an increasing and decreasing trend, but the increasing trend is dominant as 89.9% and 79.1% of the total area using AAT and SAT respectively are significant increasing trend. One the other hand, only 0.45% and 0.36% of the total area has significant decreasing trend using AAT and SAT respectively and the rest of the area has no significant trend. The seasonal adjusted method showed most of the no trend areas is distributed through the eastern part and the far western part of Turkey. The Annual aggregated trend showed similar pattern with largest no trend area centered in the far eastern part of Turkey. The gradient analysis showed decreasing in the magnitude of the positive NDVI trend when moving from the west to the east, and no specific pattern in the south north direction.

Comparison of Spatial Interpolation Methods of Precipitation and Temperature Using Multiple Integration Periods

Eight spatial interpolation methods are used to interpolate precipitation and temperature over several integration periods in a local scale. The methods used are inverse distance weighting (IDW), Thiessen polygons (TP), trend surface analysis, local polynomial interpolation, thin plate spline, and three Kriging methods: ordinary, universal, and simple (OK, UK, and SK). Daily observations from 17 stations in the Seyhan Basin, Turkey, between 1987 and 1994 are used. A variety of parameters and models are used in each method to interpolate surfaces for several integration periods, namely, daily, monthly and annual total precipitation; monthly and annual average precipitation; and daily, monthly and annual average temperature. The performance is assessed using independent validation based on four measurements: the root mean squared error, the mean squared relative error, the coefficient of determination (r2), and the coefficient of efficiency. Based on these validation measurements, the method with smallest errors for most of the integration periods concerning both precipitation and temperature is IDW with a power of 3, whereas TP has the highest errors. The Gaussian model is found superior than other models with less errors in the three Kriging methods for interpolating precipitation, but no specific model is better than another for modeling temperature. UK with elevation as the external drift and SK with the mean as an additional parameter show no superiority over OK. For precipitation, annual average and monthly totals are found to be the worst and best modeled integration periods respectively, with the monthly average the best for temperature.