[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117554-en":3,"doc-seo-117554-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117554,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Classification Of Irrigation Water Based On Machine Learning Approach","This thesis integrates advanced machine learning models into irrigation water classification, grounded in groundwater quality assessment using the Irrigation Water Quality Index (IWQI) and subsequent groundwater categorization. The research extends toward interpretable ML models to enable reliable predictions and support more efficient water-quality assessment methodologies. Machine learning is applied to simulate IWQI accurately and to streamline economic monitoring for irrigation water management. Results carry meaningful implications for water resource management, strengthening decision-making and sustainable practices for farmers and stakeholders.","PEOPLE ’S DEMOCRATIC REPUBLIC OF ALGERIA  \nالجمهوريــة الجزائريــة الديمقراطيــة الشعبيــة  \nMinistry of Higher Education and Scientific Research  \nوزارة التعليم العالي والبحث العلمي  \nUniversity of Mohamed Khider Biskra  \nCivil and Hydraulic Engineering Department Faculty of Science and Technology  \nRef:    \nجامعة محمد خيضر- بسكرةقسم الهندسة المدنية و الريكلية العلوم و التكنولوجياالمرجع…:  \nDissertation submitted for the degree of Doctor of Philosophy 3rd cycle LMDIn : Hydraulic  \nOption : Water resources  \nTheme  \nClassification Of Irrigation Water Based On Machine Learning Approach  \nPresented by :  \nAymen ZEGAAR  \nPublicly Defended, the 27/02/2025, before the jury composed of:  \nMs.  \nMs.  \nMr.  \nMr.  \nMr.  \nLeila Youcef Samira Ounoki Abdelmoutie Telli Djeddou Messaoud Soheyb Ayad  \nProfessor  \nProfessor MCA Professor MCA  \nPresident Superviser Co-Superviser Examiner  \nExaminer  \nTo my Parents,  \nYour unwavering love, encouragement, and enduring sacrifices have been the bedrock of my journey. This achievement is a testament to your profound impact on my life and aspirations.  \nI dedicate this work to you with deep gratitude.  \nTo my Brothers and Sisters,  \nIn every challenge and triumph, you’ve been my steadfast companions. Your unwavering support and shared joys have made this academic pursuit a richer experience. I dedicate this  \nwork to our collective bond.  \nTo the Wider Family,  \nFor your understanding, encouragement, and shared pride in my accomplishments. Your support, spanning generations, has been a source of strength. I dedicate this work to the extended family that stands as a pillar of support.  \nTo my Supervisors,  \nYour guidance, wisdom, and mentorship have been instrumental in shaping the trajectory of my PhD journey. I am grateful for your expertise and unwavering support. I dedicate this  \nwork to your invaluable contributions.  \nTo All Those Who Contributed Directly or Indirectly, To friends, colleagues, and mentors who have played a role, seen and unseen, in the creation of this thesis. Your varied contributions have enriched my academic experience. I dedicate this work to the collaborative spirit that made it possible. This work is dedicated to those who have been my constants, my pillars of strength, and my sources of inspiration throughout the journey of my Ph.D. Your collective presence has made  \nthis accomplishment all the more meaningful.  \nDeclaration  \nI hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and Acknowledgements.  \nAymen ZEGAAR March 2025  \nAcknowledgements  \nThe authors express gratitude to the anonymous referees for their valuable remarks and comments. Special thanks are extended to the water analysis laboratory of the Algerian Water Company (ADE) in Msila for providing access to the necessary data for this work. The authors acknowledge the support received from the Directorate-General for Scientific Research and Technological Development (DGRSDT) in Algeria, as well as the LARHYSS laboratory of Biskra University.  \nملخص  \nهذه الدراسة تهدف لاستغلال نماذج التعلم الآلي المتقدمة في تصنيف مياه الري. بداية من تقييم جودة المياه الجوفية من خلال IWQI وتصنيفها، ثم يتطور البحث للاستفادة من نماذج التعلم الآلي القابلة للتفسير من اجل التنبؤ. يمثل هذا البحث تحولاجذريا في منهجيات تقييم جودة المياه، مبرزا تحقيق كفاءة أكبر. تطبيق التعلم الآلي يضمن محاكاة دقيقة لمؤشر جودة مياهالري IWQI وطريقة اقتصادية محسنة لادارته. يحمل هذا العمل آثاراً كبيرة على إدارة موارد المياه، ويعود بالفائدة بشكلخاص على الفلاحين واصحاب القرار، مما يسهم في تطور ممارسات إدارة المياه المستدامة، ويقدم وجهة نظر محورية فيتقييم جودة مياه الري باستخدام التعلم الآلي.  \nالك","cbCaio1I1deph4Sc","https://ap.wps.com/l/cbCaio1I1deph4Sc","pdf",23248134,1,119,"English","en",105,"# Abstract\n# Keywords\n# Declaration\n# 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