Irrigation Runoff Modeling-Regression Based System

Agricultural sector uses 70 % of all fresh water that is use globally, however up to 95 % in the developing countries to meet with the challenges of growing demand of 70 % more food for estimated population of 9.1 billion in 2050. It is also estimated that 14 % more fresh water will be needed to withdraw for agriculture purposes in the next 30 years. It is evident from literature that precision irrigation systems outperformed the traditional/ surface irrigation system and save water up to 50 %. In this report, NRCS has been used and a two-farm scenario has been considered in two different watersheds at the slope level which has sprinkler irrigation system installed. As the sprinkler irrigation event happens water will flow from farm A through an outlet point / gateway to the farm B, it must be learnt and predicated before going to inlet stream of farm B's sprinkler irrigation system. Multiple wireless sensors and machine learning algorithms have been used for data extraction from soil moisture , crop stage on farm A and predication of runoff volume and runoff time has been calculated to utilize in precision irrigation system of farm B efficiently to save water waste.

Autorentext

Marwan Khan sta attualmente svolgendo il suo dottorato di ricerca presso l'Università di Southampton UK. I suoi interessi di ricerca sono l'irrigazione di precisione, WSN e la modellazione del deflusso dell'irrigazione utilizzando algoritmi di apprendimento automatico. È anche in servizio come docente presso il Dipartimento di Informatica all'Abdul Wali khan University Mardan (AWKUM) Pakistan.