Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm
Clonal selection algorithm; Multi-dimensional LSTM; Rainwater harvesting; Resource optimization; Sustainability; Water management; Water scarcity mitigation
Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications. 2024 Elsevier B.V.
Raghava Rao N.; Pokkuluri Kiran S.; Amena I T.; Senthilkumar A.; Sivakumar R.; Ashok Kumar M.; Velusamy S.
Groundwater for Sustainable Development, Vol-25
https://doi.org/10.1016/j.gsd.2024.101167 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189021911&doi=10.1016%2fj.gsd.2024.101167&partnerID=40&md5=6d346877ff14cf448e7e80b8efab687d
Raghava Rao N., Department of Information Technology, Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500043, India; Pokkuluri Kiran S., Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Vishnupur, AP, Bhimavaram, India; Amena I T., Department of Civil Engineering, Deogiri College of Engineering and Management (DIETMS), Maharashtra, Aurangabad, India; Senthilkumar A., School of Science and Information Technology, Skyline University Nigeria, Kano State, Nigeria; Sivakumar R., Department of Statistics and Data Science, CHRIST(Deemed to be University), Bangalore, India; Ashok Kumar M., Department of Computer Science and Software Engineering, Skyline University Nigeria, Kano, Nigeria; Velusamy S., Department of Civil Engineering, Kongu Engineering College, Perundurai, 638060, India