Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-13
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
23 Jan 2017
Development of a Spatial Hydrologic Soil Map Using Spectral Reflectance Band Recognition and a Multiple-Output Artificial Neural Network Model
Khamis Naba Sayl1,3, Haitham Abdulmohsin Afan1, Nur Shazwani Muhammad1, and Ahmed ElShafie2 1Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
2Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
3Department of Dams and Water Resources, Engineering College, University of Anbar, Ramadi, Iraq
Abstract. Soil type is important in any civil engineering project. Thorough and comprehensive information on soils in both the spatial and temporal domains can assist in sustainable hydrological, environmental and agricultural development. Conventional soil sampling and laboratory analysis are generally time-consuming, costly and limited in their ability to retrieve the temporal and spatial variability, especially in large areas. Remote sensing is able to provide meaningful data, including soil properties, on several spatial scales using spectral reflectance. In this study, a multiple-output artificial neural network model was integrated with geographic information system, remote sensing and survey data to determine the distributions of hydrologic soil groups in the Horan Valley in the Western Desert of Iraq. The model performance was evaluated using seven performance criteria along with the hydrologic soil groups developed by the United States Geological Survey (USGS). On the basis of the performance criteria, the model performed best for predicting the spatial distribution of clay soil, and the predicted soil types agreed well with the soil classifications of the USGS. Most of the samples were categorized as sandy loam, whereas one sample was categorized as loamy sand. The proposed method is reliable for predicting the hydrological soil groups in a study area.
The discussion paper was formally withdrawn.


Citation: Sayl, K. N., Afan, H. A., Muhammad, N. S., and ElShafie, A.: Development of a Spatial Hydrologic Soil Map Using Spectral Reflectance Band Recognition and a Multiple-Output Artificial Neural Network Model, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-13, 2017.
Khamis Naba Sayl et al.
Khamis Naba Sayl et al.
Khamis Naba Sayl et al.

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This paper presents a new methodology by integrating the multiple-output artificial neural network model, geographic information system, remote sensing and survey data to determine the distributions of hydrologic soil groups in the Horan Valley, Iraq. The model performed best for predicting the spatial distribution of clay soil and the predicted soil types agreed well with the USGS soil classifications. The proposed method was demonstrated to be reliable for predicting the soil groups in a study.
This paper presents a new methodology by integrating the multiple-output artificial neural...
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