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Naveed Jeelani Khan, Umar Bashir, Asif Adil
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10.1109/wispnet51692.2021.9419456
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Energy and policy considerations for modern deep learning research
10.1609/aaai.v34i09.7123 · doi-reference
Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models
10.1007/s00521-021-06666-4 · doi-reference
Emerging trends in global freshwater availability
10.1038/s41586-018-0123-1 · doi-reference
The 2015-2017 cape town drought: Attribution and prediction using machine learning
10.1016/j.procs.2018.10.323 · doi-reference
Overcoming the challenges of water, waste and climate change in Asian Cities
10.1007/s00267-019-01137-y · doi-reference
The Flint water crisis
10.1002/wat2.1420 · doi-reference
Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions
10.1016/j.heha.2024.100114 · doi-reference
Learning from intermittent water supply schedules: Visualizing equality, equity, and hydraulic capacity in Bengaluru and Delhi, India
10.1016/j.scitotenv.2023.164393 · doi-reference
Internet of Things (IoT) of smart homes: Privacy and security
10.1155/2024/7716956 · doi-reference
A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints
10.3390/agriculture14071141 · doi-reference
Translating climate risk assessments into more effective adaptation decision-making: The importance of social and political aspects of place-based climate risk
10.1016/j.envsci.2024.103705 · doi-reference
Water policy review: Ensuring sustainable water management for India
10.1016/j.jenvman.2025.125823 · doi-reference
Water quality assessment of a river using deep learning Bi-LSTM methodology: Forecasting and validation
10.1007/s11356-021-13875-w · doi-reference
Explainable artificial intelligence for sustainable urban water systems engineering
10.1016/j.rineng.2025.104349 · doi-reference
A new digital twin for climate change adaptation, water management, and disaster risk reduction (HIP digital twin)
10.3390/w15010025 · doi-reference
Interpreting black-box models: A review on explainable artificial intelligence
10.1007/s12559-023-10179-8 · doi-reference
Deep reinforcement learning for real-time optimization of pumps in water distribution systems
10.1061/(asce)wr.1943-5452.0001287 · doi-reference
Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: A review
10.1007/s10311-024-01748-w · doi-reference
Leveraging the collaborative power of AI and citizen science for sustainable development
10.1038/s41893-024-01489-2 · doi-reference
Climate stress tests as a climate adaptation information tool in Dutch municipalities
10.1016/j.crm.2021.100318 · doi-reference
10.1007/978-3-031-62079-9_22
10.1007/978-3-031-62079-9_22 · doi-reference
Review of smart water management: IoT and AI in water and wastewater treatment
10.30574/wjarr.2024.21.1.0171 · doi-reference
Monitoring inequality in water access: Challenges for the 2030 Agenda for Sustainable Development
10.1016/j.scitotenv.2020.138746 · doi-reference
Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems?
10.1002/etc.3220 · doi-reference
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes
10.1016/j.jhydrol.2022.128618 · doi-reference
Examining daily closing price prediction of the NSE index using an optimized artificial neural network : A study of stock market
10.3329/jsr.v17i1.74640 · doi-reference
Addressing gaps in data on drinking water quality through data integration and machine learning: evidence from Ethiopia
10.1038/s41545-023-00272-8 · doi-reference
Green artificial intelligence initiatives: Potentials and challenges
10.1016/j.jclepro.2024.143090 · doi-reference