Abstract Middle-East region is mostly characterized by a hot and dry climate, vast deserts and long coastlines. Deserts cover large areas, while agricultural lands are described as small areas of arable land under perennial grass pastures or crops. In view of the harsh climate and falling ground-water level, it is critical to identify which agriculture produce to grow, and where to grow it? The traditional methods used for this purpose are expensive, complex, prone to subjectivity, risky and are time- consuming; this points to the need of exploring novel IT techniques using Geographic Information Systems (GIS). In this paper, we present a data-driven stand-alone flexible analysis environment i.e., Spatial Prediction and Overlay Tool (SPOT). SPOT is predictive spatial data mining GIS tool designed to facilitate decision support by processing and analysing agro- meteorological and socio-economic thematic maps and generating crop cultivation geo-referenced prediction maps by predicative data mining. In this paper, we present a case study of Saudi Arabia by using decade old wheat cultivation data, and compare the historically uncultivated regions predicted by SPOT with their current cultivation status. The prediction results were found to be promising after verification in time and space using latest satellite imagery followed by on-site physical ground verification using GPS.

References

[1] Al-Ahmadi F. and Hames A., Comparison of Four Classification Methods to Extract Land Use and Land Cover from Raw Satellite Images for Some Remote Arid Areas, Kingdom of Saudi Arabia, Journal of King Abdulaziz University, Earth Sciences, vol. 20, no.1, pp. 167-191, 2008.

[2] Al-Jarrah O. and Abu-Qdais H., Municipal Solid Waste Landfill Siting using Intelligent System, Waste Management, vol. 26, no. 3, pp. 299-306, 2006.

[3] Anonymous, http://estation.jrc.ec.europa.eu/, Last Visited, 2015.

[4] Anonymous, http://grass.osgeo.org, Last Visited, 2014.

[5] Anonymous, http://www.exelisvis.com/, Last Visited, 2015.

[6] Anonymous, http://www.exelisvis.com/ProductsServices/Jagw ire.aspx, Last Visited, 2015.

[7] Anonymous, http://www.fao.org/giews/english/windisp/windis p.htm, Last Visited, 2015.

[8] Anonymous, http://www.tradingeconomics.com/saudi- arabia/arable-land-percent-of-land-area-wb- data.html, Last Visited, 2015.

[9] Auria L. and Rouslan A., Support Vector Machines (SVM) as a Technique for Solvency Analysis, Deutsches Institut f r Wirtschaftsforschung (DIW), Berlin, 2008.

[10] Chung C. and Fabbri A., Probabilistic Prediction Models for Landslide Hazard Mapping, Photogrammetric Engineering and Remote Sensing, vol. 65, no. 12, pp. 1389-1399, 1999.

[11] Chung C. and Fabbri A., The Representation of Geoscience Information for Data Integration, Nonrenewable Resources, vol. 2, no. 2, pp. 122-139, 1993.

[12] Chung C. and Fabbri A., Validation of Spatial Prediction Models for Landslide Hazard Mapping, Natural Hazards, vol. 30, no. 3, pp. 451-472, 2003.

[13] Dem ar D., D eroski S., Henning Krogh P., and Larsen T., Using Machine Learning to Predict the Impact of Agricultural Factors on Communities of Soil Microarthropods, Metodolo ki Zvezki, vol. 2, no. 1, pp. 147-159, 2005.

[14] Eerens H., Haesen D., Rembold F., Urbano F., Tote C., and Bydekerkedf L., Image Time Series Processing for Agriculture Monitoring, Environmental Modelling and Software, vol. 53, pp. 154-162, 2014.

[15] Eldrandaly K., Developing a GIS-Based MCE Site Selection Tool in ArcGIS Using COM Technology, The International Arab Journal of Information Technology, vol. 10, no. 3, pp. 276- 282, 2013.

[16] Eldrandaly K., Spatial Decision Making: An Intelligent GIS-Based Decision Analysis Approach, VDM Verlag, 2010.

[17] Kannan P. and Hemalatha K., Agro Genius: An Emergent Expert System for Querying Agricultural Clarification Using Data Mining Technique, International Journal of Engineering and Science, vol. 1, no. 11, pp. 34- 39, 2012.

[18] Kantardzic M., Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons Publishing, 2003.

[19] Killmann W., Climate Change and Food Security: a Framework Document, Food and Agriculture Organization, 2008.

[20] Kim Y., Comparison of the Decision Tree, Artificial Neural Network, and Linear Regression Methods based on the Number and Types of Using Data Mining for Predicting Cultivable Uncultivated Regions in the Middle East 1041 Independent Variables and Sample Size, Expert Systems with Application, vol. 34, no. 2, pp. 1227-1234, 2008.

[21] Lee C., Gutierrez F., and Dou D., Calculating Feature Weights in Naive Bayes with Kullback- Leibler Measure, in Proceedings of IEEE 11th International Conference on Data Mining, Vancouver, pp. 1146-1151, 2011.

[22] Linear Regression Model, CAMO, http://www.camo.com/rt/Resources/linear_regres sion_model.html, Last Visited, 2015.

[23] Lu K. and Yang D., Image Processing and Image Mining using Decision Trees, Journal of Information Science and Engineering, vol. 25, pp. 989-1003, 2009.

[24] Nassar M., Kanaan G., and Awad H., Framework for Analysis and Improvement of Data-fusion Algorithms, in Proceedings of 2nd IEEE International Conference on Information Management and Engineering, Chengdu, pp. 379-382, 2010.

[25] Nayab N. Disadvantages to Using Decision Trees. http://www.brighthubpm.com/project- planning/106005-disadvantages-to-using- decision-trees/, Last Visited, 2015.

[26] Neteler M. and Mitasova H., Open Source GIS: A GRASS GIS Approach, Springer, 2008.

[27] Neteler M., Bowman M., Landa M., and Metz M., GRASS GIS: A Multi-purpose Open Source GIS, Environmental Modelling and Software, vol. 31, pp. 124-30, 2012.

[28] Quinlan J., C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 2014.

[29] Rotational Value of Alfalfa, http://www.americasalfalfa.com/alfalfa/media/P DFs/Rotational_Value_v4, Last Visited, 2014.

[30] Ruiz L., Recio J., Hermosilla T., and Sarria A., Identification of Agricultural and Land Cover Database Changes using Object-oriented Classification Techniques, in Proceedings of 33rd International Symposium on Remote Sensing of Environment, Stresa, 2009.

[31] Siqueira J., Pa o T., Silvestre J., Santos F., Falc o A., and Pereira L., Generating Fuzzy Rules by Learning from Olive Tree Transpiration Measurement-An Algorithm to Automatize Granier Sap Flow Data Analysis, Computers and Electronics in Agriculture, vol. 101, pp. 1- 10, 2014.

[32] Spatial data warehouse. http://www.spatial- eye.com/Engels/Applications/Spatial- DWH/page.aspx/117, Last Visited, 2015.

[33] Steeg van de J. and Tibbo M., Livestock and Climate Change in the Near East Region, Food and Agriculture Organization, 2012.

[34] Tan P., Steinbach M., Karpatne A., and Kumar V., Introduction to Data Mining, Addison- Wesley Publishing, 2006.

[35] Witten I. and Frank E., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishing, 2005.

[36] Xia H., Liu H., and Zheng C., A Markov- Kalman Model of Land-Use Change Prediction in XiuHe Basin, China, in Proceedings of Geo- Informatics in Resource Management and Sustainable Ecosystem, China, pp. 75-85, 2013.

[37] Zhao Y. and Zhang Y., Comparison of Decision Tree Methods for Finding Active Objects, Advances in Space Research, vol. 41, no. 12, pp. 1955-1959, 2007. 1042 The International Arab Journal of Information Technology, Vol. 15, No. 6, November 2018 Ahsan Abdullah did his PhD in data mining from the University of Stirling UK, MSc Computer Sciences and MSc Computer Engineering both from the University of Southern California USA and BS Electrical Engineering (with distinction) from Univ. of Engineering and Tech. Lahore. He is at the Dep. of IT, Faculty of Computing and IT, King Abdulaziz University, Jeddah. He is the PI of the project funded by the King Abdulaziz City for Science and Technology titled Using Data Mining for Predicting Long Term Productivity of Pastures in the Kingdom of Saudi Arabia . He has been the lead guest editor of special issue on Big Data of the Springer Cognitive Computation Journal with IF=3.47; in 2015 he had seven ISI indexed impact factor journal papers as first author. Ahmed Bakhashwain did his PhD in Plant Physiology from King Saud University Riyadh, MSc in Renewable Natural Resources from University of Arizona, USA and BS in Biological Science from, King Abdulaziz University, Jeddah. He is currently serving in the Department of Arid Regions Agriculture Faculty of Meteorology, Environment and Arid Land Agriculture King Abdulaziz University, Jeddah. He is the CO-I of the KACST funded project with the primary author. Abdullah Basuhail did his PhD in Computer Science, Digital Image Processing (Wavelet Transform) from Florida Institute of Technology, USA. He is currently serving in the Department of Computer Science Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah He is the CO-I of the KACST funded project with the primary author. Ahtisham Aslam did his PhD in Semantic Web from University of Leipzig Germany, MS in Computer Science from Hamdard University Lahore and BSc from the Punjab University Lahore. He is currently serving in the Dept. of IS, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah.