Coskun Ozkan

Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration

The study investigates the ability of artificial neural networks (ANN) with artificial bee colony (ABC) algorithm in daily reference evapotranspiration (ET0) modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN–ABC model so as to estimate ET0 obtained using the FAO-56 Penman–Monteith (PM) equation. In the first part of the study, the accuracy of ANN–ABC models is compared with those of the ANN models trained with Levenberg–Marquardt (LM) and standard back-propagation (SBP) algorithms and those of the following empirical models: The California Irrigation Management System (CIMIS) Penman, Hargreaves, and Ritchie methods. The mean square error (MSE), mean absolute error (MAE) and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Based on the comparison results, the ANN–ABC and ANN–LM models are found to be superior alternative to the ANN–SBP models. In the second part of the study, the potential of the ANN–ABC, ANN–LM, and ANN–SBP models in estimation ET0 using nearby station data is investigated.

The Artificial Bee Colony algorithm in training Artificial Neural Network for oil spill detection

Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

Surface interpolation by adaptive neuro-fuzzy inference system based local ordinary kriging

A new approach to the Ordinary Kriging interpolation method based on the combination of local interpolation and variogram modelling with Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for surface interpolation. In this method, the experimental variogram is modelled by ANFIS and this model is used to interpolate the unknown values of specific points in a new local manner. In this local way, all the unknown points are grouped based on each reference point. As the study data, two types of data sets coming from mathematical functions and a 3D scanning system are used. The tests show that the proposed method produces better performances for all data sets in comparison to the well known and highly approved interpolation methods; Ordinary Kriging, Triangle Based Cubic and Radial Basis Function-Multiquadric. Moreover, by the proposed method the computational complexity impressively decreases compared to the global Ordinary Kriging.

A New Artificial Intelligence Optimization Method for PCA Based Unsupervised Change Detection of Remote Sensing Image Data

In this study, a new artificial intelligence optimization algorithm, Differential Search (DS), was proposed for Principal Component Analysis (PCA) based unsupervised change detection method for optic and SAR image data. The model firstly computes an eigenvector space using previously created k × k blocks. The change detection map is generated by clustering the feature vector as two clusters which are changed and unchanged using Differential Search Algorithm. For clustering, a cost function is used based on minimization of Euclidean distance between cluster centers and pixels. Experimental results of optic and SAR images proved that proposed approach is effective for unsupervised change detection of remote sensing image data.

Animation‐based learning of map projections in geomatics engineering

Using educational animations is a very effective way of conveying the knowledge and increasing the understanding in a lecture. Teaching and learning of map projections are among the most difficult topics in the education of Geomatics Engineering for both lecturers and students. In this article, it is aimed to visualize the mapping projections with educational animations developed with Matlab Mapping toolbox to provide better understanding of students in the Geomatics Engineering.

A New SEBAL Approach Modified with Backtracking Search Algorithm for Actual Evapotranspiration Mapping and On-Site Application

Abstract Actual evapotranspiration is one of the most important component for efficient water management and planning. Until recently, evapotranspiration computations and measurements have been performed locally. But, in recent years, actual evapotranspiration computations can be calculated regionally thanks to improvements on remote sensing discipline and satellites. Surface Energy Balance Algorithm for Land (SEBAL) is one of the most commonly preferred technics for actual evapotranspiration mapping. However, this algorithm has some difficulties such as mismatch with Landsat 8 and hot–wet pixel selection which require expert knowledge. In this paper, a novel SEBAL based approach (SEBAL-BSA), which can use Landsat 8 images as data and can automatically perform hot–wet pixel selection using Backtracking Search Algorithm (BSA) with ground control points, has been proposed. SEBAL-BSA was developed and tested using January 22–2015, April 28–2015 and July 01–2015 dated Landsat 8 images in Akarsu Irrigation Water User Association command area in the Çukurova Region of Turkey and actual evapotranspiration mapping was realized without albedo measurements. Accuracy of SEBAL-BSA has been examined by comparing values of predefined ground truth points and values obtained from proposed approach. According to results of parametric matched pairs T test and nonparametric Wilcoxon sign rank test, SEBAL-BSA is highly successful. Applications also show that the SEBAL-BSA is a user-friendly approach for institutions and organizations related to water authorization and management.

Erratum to: A New Approach for Modeling Sediment-Discharge Relationship: Local Weighted Linear Regression

After publication of this article we received a request from Dr. Coskun Ozkan to have his name removed from the author list. Due to unforeseen circumstances Dr. Coskun Ozkan did not have the opportunity to participate in the final approval of the manuscript and missed the opportunity to declare that he withdrew from this paper. Dr. Ozgur Kisi agrees with this change. The correct author list is as shown below. WaterResourManage(2017)31:25 DOI 10.1007/s11269-016-1551-z