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Machine Learning Approaches to Climate-Smart Irrigation Planning
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Author(s): Usharani Bhimavarapu (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India)
Copyright: 2026
Pages: 30
Source title:
Computational Intelligence and Optimization Methods for Sustainable Water Management
Source Author(s)/Editor(s): Yassine Ezaier (Hassan II University, Casablanca, Morocco), Rajae Gaamouche (Moroccan School of Engineering Sciences, Rabat, Morocco)and Mohamed Lahby (Hassan II University, Casablanca, Morocco)
DOI: 10.4018/979-8-3373-2700-6.ch009
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Abstract
Climate change poses existential risks to water management using a sustainable approach, particularly in agricultural-dependent areas such as Andhra Pradesh, India. In this context, using Machine Intelligence, which is Artificial Neural Networks (ANNs), this research investigates the application of Machine Intelligence in aiding climate adaptation for water management. The method involves the integration of multi-source data sets, including climate parameters (2020–2024), agricultural-based data on paddy farming, and geographic information for irrigation and topography. Preprocessing procedures maintained uniformity and compatibility among such datasets, after which Particle Swarm Optimization (PSO) was applied for feature selection to identify the most impactful factors influencing water supply and crop yield. The ANN model trained well and precisely forecasted irrigation demand and pinpointed possible periods of water stress, allowing localized real-time decision-making support.
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