Presenting an AI-based Adaptive Decision Support System for Selecting Executive Operations in Watersheds: A Novel Approach to Eco-friendly Water and Soil Resource Management

Abstract In recent decades, optimal watershed management, as one of the key components in water and soil resource conservation, has faced serious challenges including ecological diversity, climate change, and limitations in human and financial resources. The selection of appropriate executive operations in each watershed – whether mechanical, biological, or managerial operations – has so far been mainly based on expert experience, traditional data analysis methods, or limited use of multi-criteria decision-making models. This situation has led to reduced project effectiveness, resource waste, and a lack of compatibility between actions and the ecological characteristics of each region. In this paper, for the first time, a novel theory titled "AI-based Adaptive Decision Support System" is introduced and elaborated. This theory is based on the development of an intelligent decision-making system that, using machine learning algorithms (including Random Forest, XGBoost, and artificial neural networks) and environmental data – climate, topography, geology, vegetation, soil type, erosion intensity, and socio-economic priorities – can suggest the optimal type of executive operation for each sub-basin in a completely adaptive and eco-friendly manner. The adaptive decision support theory has four key features: 1. Dynamic learning from local and regional data; 2. Adaptability to changing climatic and human conditions; 3. Integration of quantitative and qualitative data with expert weighting; 4. Scalability as a national or global system at various scales. To validate this theory, an experimental system was implemented in several watersheds across the country, including the Qomrud River basin (Qom Province), Zarand Plain in Kerman, and the Doroudzan Dam watershed in Fars. Climatic, topographic, vegetation, and erosion data were fed into the system, and the proposed outputs were compared with the results of actual operations and traditional decisions. The results indicated that the accuracy of the system's recommendations in selecting the type of executive operations matched over 85% of the best past performances and, in many cases, provided new and more effective suggestions.This article not only provides a novel model for watershed management in Iran but also lays the groundwork for developing an international standard in natural resource planning during climate change and resource scarcity. With further development of this theory, it can also be utilized in other semi-arid and arid countries worldwide. Furthermore, this framework has the capability to connect to GIS systems, remote sensing databases, and early warning systems for floods and droughts.