قسم التربة والمياه

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حول قسم التربة والمياه

 افتُتح القسم مع إنشاء كلية الزراعة سنة 1966م، وقد اهتم بالتوسع في مختلف التخصصات المتعلقة بعلوم التربة والمياه وبإعداد الكوادر العلمية القادرة على إدارة وتسيير المشاريع الزراعية أو مواصلة دراستها والحصول على درجات ومؤهلات عالية، كما اهتم بتجهيز المختبرات وتزويدها بمختلف الأجهزة والمعدات الحديثة والمتطورة لاستخدامها في مجالات البحث والدراسة.

حقائق حول قسم التربة والمياه

نفتخر بما نقدمه للمجتمع والعالم

73

المنشورات العلمية

26

هيئة التدريس

52

الطلبة

0

الخريجون

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يوجد بـقسم التربة والمياه أكثر من 26 عضو هيئة تدريس

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د. ميرفت الطاهر رمضان بن محمود
قسم التربة والمياه - كلية الزراعة طرابلس
الصفحة الشخصية
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أ.د. أحمد إبراهيم محمد خماج
قسم التربة والمياه - كلية الزراعة طرابلس
الصفحة الشخصية
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د. حمدي عبدالخالق علي الزرقاني
قسم التربة والمياه - كلية الزراعة طرابلس
الصفحة الشخصية
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د. يوسف صالح المبروك حامد
قسم التربة والمياه - كلية الزراعة طرابلس
الصفحة الشخصية
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د. حمدي عبدالخالق علي الزرقاني

Dr. Hamdi A. Zurqani is one of the faculty members at the Department of Soil and Water Sciences, Faculty of Agriculture, University of Tripoli, Tripoli, Libya. Dr. Zurqani is a recognized expert as a result of his internationally acclaimed work in the areas of Environmental Information Science, Remote Sensing, Land Evaluation, Sustainability, Pedology, and Soil Science Education. He has conducted research across the world, including the United States of America, and Africa. Dr. Zurqani is a distinguished soil scientist with a wide range of scientific and working experiences in Libya and abroad. He received his M.Sc. (2010) from the University of Tripoli, Tripoli, Tripoli, Libya, and Ph.D. (2019) from Clemson University, Clemson, SC, USA. His major research and teaching activities at the University of Tripoli have focused mainly on Soil Genesis and Classification and the Environmental Information Sciences (Remote Sensing and Geographic Information System). He has published broadly in many journals (e.g., Nature “Scientific Reports”, Geoderma; International Journal of Applied Earth Observation and Geoinformation; Journal of Geological Sciences; Land; Frontiers in Environmental Science; Communications in Soil Science and Plant Analysis; and others). Dr. Zurqani is a member of the Editorial Board for Remote Sensing (MDPI) Journal, counseling outcome and research evaluation. He also was appointed to serve as a Guest Editor for the Special Issue "Applications of Remote Sensing in Earth Observation and Geo-Information Science". In addition, Dr. Zurqani conducted peer-review for many journals including Journal of Environmental Informatics, Applied Sciences, SN Applied Sciences, Remote Sensing, Heliyon, Geosciences, Land, Water, Agronomy, Agriculture, Sustainability, Arid Land Research and Management, International Journal of Environmental Research and Public Health, Natural Hazards, and Conference of the Arabian Journal of Geosciences. He is also one of the authors of the lab manual entitled “GIS Exercises for Natural Resource Management”. Dr. Zurqani has been the recipient of numerous awards and honors: Recipient of Douglas R. Phillips Award for Graduate Students, Department of Forestry and Environmental Conservation, Clemson University, April 12, 2019; the First Place Best Judged Poster (CAFLS) at the GRADS 2019: Clemson Student Research Forum on April 4, 2019; the Second Place Poster at the 11th Clemson Biological Sciences Annual Student Symposium, April 6, 2019; the Second Place Best Judged Poster at the Clemson Student Research Forum on April 4, 2018; and the Third Place Poster at the 9th Clemson Biological Sciences Annual Student Symposium, February 25, 2017. Dr. Zurqani conducts cutting-edge research in the field of environmental information science, remote sensing, land use management/planning, change detection of landscape degradation, and geographic information system (GIS) models. He has focused on his research efforts on the development of new technologies in the field of environmental information sciences, geo-intelligence (advanced geo-information science and earth observation, machine and deep learning, and big data analytics), remote sensing, land evaluation, pedology, land use management/ planning, monitoring and evaluating sustainable land management, change detection of landscape degradation, and geographic information system models.

منشورات مختارة

بعض المنشورات التي تم نشرها في قسم التربة والمياه

Artificial Neural Networks to predict decreasing saturated hydraulic conductivity in soils irrigated with saline-sodic water.

Multilayer Artificial Neural Networks (ANNs) with the backpropagation algorithm were used to estimate the decrease in relative saturated conductivity due to an increase in sodicity and salinity. Data from the literature on the relative saturated hydraulic conductivity measured using water having levels of sodicity and salinity in different types of semiarid soils were used. The clay content of these soils is predominantly montmorillonite. The input data consisted of clay percentage, cation exchange capacity, electrolyte concentration, and estimated soil exchangeable sodium percentage at equilibrium stage with the solution applied. The data was divided into three groups randomly to meet the three phases required for developing the ANNs model (i. e. training, evaluation, and testing).The activation function selected was the TANSIG layer in the middle, while the exit function was the PURELIN layer. The comparisons between the experimental and predicted data on relative saturated hydraulic conductivity during training and testing phases showed good agreement. This was evident from the statistical indicators used for the evaluation process. For the training phase, the values of mean absolute error (MAE), root mean square error (RMSE), the correlation coefficient (r) and the determination coefficient (R2) were 0.08, 0.13, 0.91, and 0.83, respectively. The performance of the ANNs model was evaluated against a part of the data selected randomly form the whole set of data collected (i. e. data not used during the model testing phase). The resultant values for MAE and RMSE, r and R2 were 0.12, 0.16, 0.82 and 0.68, respectively. It should be noted that many factors were not considered, such as soil pH, type of clay, and organic matter, due to the limitations of the data available. Using these factors as input in ANNs might improve model predictions. However, the results suggested that the ANNs model performs well in soils with very low levels of organic matter.
Younes Daw Ezlit, Ahmed Ibrahim Ekhmaj, Mukhtar Mahmud Elaalem(5-2014)
Publisher's website

Estimation of Sunshine Duration using Statistical Approach:‎ Libya As A case Study

Sunshine duration (SD) is an essential atmospheric indicator which is used in many agriculture, ‎architects and solar energy applications. In many situations where data of sunshine duration may not be ‎available due to temporal and financial constraints, developing alternative indirect methods based on ‎theoretical considerations for determining SD are essentially required. In this study, seven models were ‎developed using stepwise regression technique to estimate monthly sunshine duration for Libya. The ‎predictors which were used as inputs differ from one model to another and they included monthly ‎cloudiness index, total day length, mean relative humidity, depth of precipitation, mean maximum ‎temperature, altitude and longitude over 16 meteorological stations spread across Libya during the ‎period of 1961 – 2000 . The evaluation of the developed models was performed using a set of data of ‎four meteorological stations representing different physiogeographic and climatic zones during 2001 ‎and against Abdelwahed and Snyder (2015) equations which were developed for estimating sunshine ‎duration for Libya. The statistical parameters of evaluation criteria included mean absolute error (MAE), ‎root mean square error (RMSE), (RMSE %) and Nash and Sutcliffe Efficiency (NSE). The linear regression ‎equation relating predicted with measured data with intercept equals zero and determination coefficient ‎‎(R2) were also used for evaluation purpose. According to the performance indicators, it was detected ‎that six of the developed models were superior to the model with one parameter (cloudiness index) in ‎estimating the sunshine hours. It was also found that all the developed models have better performance ‎in estimating the sunshine duration as compared with Abdelwahed and Snyder (2015) equations. ‎However, due to its few required variables, a model with two parameters (cloudiness index and total ‎day length) is sufficient and can be used with confidence in estimating sunshine duration for Libya. ‎ Keywords: Sunshine duration, Stepwise regression, Statistical model.‎ arabic 15 English 69
ِAhmed Ibrahim Ekhmaj, Milad Omran Alwershefani(12-2016)
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دراسة حساسية الخزانات الجوفية السطحية للتلوث باستخدام نموذج دراستك (حالة دراسية: سهل الجفارة)

تعتبر المياه الجوفية المصدر الرئيس ي المستخدم لإمداد غالبية المجالات الزراعية والصناعية والمدنية في منطقة سهل الجفارة. وقد أدى نمو النشاطات الزراعية والصناعية فى المنطقة إلى وجود العديد من النواتج والمخلفات الصناعية التي تترك فى العادة فوق سطح التربة، مما قد يعرض مخزون الماء الجوفي السطحي للتلوث، لذلك تم فى هذه الدراسة استخدام نموذج دراستك ( DRASTIC ( فى بيئة نظم المعلومات الجغرافية من أجل إنتاج خرائط مكانية للتنبؤ بمدى حساسية المياه الجوفية السطحية للتلوث، وتحديد درجات حساسية هذه الخزانات للتلوث، ووضع خرائط توزيع مكاني لكافة خصائص نموذج دراستك من خلال تطبيق طريقة مقلوب المسافة الوزنية ) IDW ( في بيئة نظم المعلومات الجغرافية. أوضحت النتائج أنه عند استخدام طريقة مقل وب المسافة الوزنية أمكن الحصول على خرائط مكانية لكافة خصائص نموذج دراستك، وأظهرت أن خرائط التوزيع المكاني لهذه الخصائص يمكن الوثوق بها، وذلك بالحصول على قيم الجذر التربيعي لمتوسط مربع الخطأ ) RMSE ( كأحد مقاييس تقييم جودة القيم المتنبأ بها؛ حيث وجدت أن قيم RMSE تقترب من الصفر، وأن التباين البسيط ما بين قيم RMSE لخصائص نموذج دراستك راجع إلى التباين فى عدد العينات المستخدمة فى هذا النموذج. أشارت النتائج – أيضا إلى أن غالبية أجزاء المنطقة صنفت إلى - متوسطة الحساسية باستثناء بعض المناطق الشمالية الغربية من منطقة الدراسة التي كانت أكثر حساسية للتلوث مقارنة بالمناطق الأخرى، وللتأكد من صلاحية نموذج دراستك ) DI ( تم معايرته مع عدد 9 من عينات مياه الآبار فى الخزان الرباعي الموجود فى المناطق المروية تم فيها تقدير تركيز النترات، وتبين أن هناك ارتباط خطي قوي بين مؤشر دراستك ) DI ( والتغير فى تركيز النترات بمنطقة الدراسة؛ حيث بلغ معامل الارتباط الخطي 0.84 ، مما يشير إلى صلاحية النتائج المتحصل عليها من هذه الدراسة، وبالتالي فإن هذه الدراسة توص ي بحماية المياه الجوفية السطحية والتقليل من خطر التلوث وذلك لاستدامة مصادر المياه الجوفية فى المنطقة.
مختار محمود مختار العالم, يونس ضو زايد الزليط, عبد المطلب غيث عبد السلام(3-2019)
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