Samah A. Massadeh
Department of Computer Engineering, FET, BAU, Amman, Jordan.
Abstract— Jordan is one of the most water‐scarce countries in the world. The Jordan valley area produces more than 60% of the Kingdom’s fruits and vegetables.
The Jordan valley area has dry and warm weather almost all around the year. These climatic conditions, coupled with low water holding capacity of the predominately-sandy soils in the Jordan valley area, make irrigation indispensable for the high quality landscapes desired by Farmers since it is the main agricultural area in Jordan.
The idea of this paper is to design an intelligent automated irrigation system for the Jordan valley area; which will ensure efficient water allocation and higher performance.
The automated irrigation system suggested is a PIC microcontroller based system that monitors air humidity, soil humidity, temperature, sun light, levels of water storage tank, and drip irrigation pipes for any cut or damage all the time, and display these parameters on an LCD screen as human interface, the user will be able to view the system status and to will be alerted for any damage, low water level or any water leakage at the drip irrigation pipes.
The suggested system is applicable and can be implemented to work for large areas like the Jordan valley area with a very low cost design and saves a lot in the terms of the water used for irrigation.
Keywords- irrigation; Jordan valley; drip irrigation; PIC; automated system; air humidity soil humidity; temperature; sun light; crops.
Nguyen Van Hung, Computer Science Division, Danang Vocational Training College, Danang City, VietNam.
Huynh Ngoc Phien, Asian Institute of Technology, BangKok, Thailand.
Tran Quoc Chien, Danang University, Danang City, VietNam.
Abstract—in streamflow simulation, the first-order gamma autoregressive (GAR(1)) model  has been found to be very effective for annual data. This paper presents some attempts to apply the GAR(1) model to the simulation of monthly streamflows. To this aim, we propose two models, namely the GAR(1)-Monthly and GAR(1)-Fragments models that will be compared with the popular Thomas-Fiering model. Based on actual data of monthly streamflows at three stations and generated series of monthly data for 1000 years, it was found that both GAR(1)-Monthly and GAR(1)-Fragments models can reproduce very well all statistical descriptors, namely mean value, standard deviation and skewness coefficient, of the historical monthly series. Moreover, the GAR(1)-Fragments model was found to perform very well in reproducing those statistical descriptors of historical annual flows also.
Keywords-GAR(1) model; GAR(1)-Monthly model; GAR(1)-Fragments model; Streamflows simulation