Home ›
Entities
› academia
› The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco
The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco
Research article (Frontiers in Water, 2023) · cited 14× · AI/ML
The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco
Summary
The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco is a scholarly article[1].
Key Facts
The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco. Retrieved May 24, 2026, from https://4ort.xyz/entity/the-contribution-of-remote-sensing-and-input-feature-selection-for-groundwater-level-prediction-using-lstm-neural-networ
MLA“The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/the-contribution-of-remote-sensing-and-input-feature-selection-for-groundwater-level-prediction-using-lstm-neural-networ.
BibTeX@misc{4ortxyz_the-contribution-of-remote-sensing-and-input-feature-selection-for-groundwater-level-prediction-using-lstm-neural-networ_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco}}, year = {2026}, url = {https://4ort.xyz/entity/the-contribution-of-remote-sensing-and-input-feature-selection-for-groundwater-level-prediction-using-lstm-neural-networ}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco — https://4ort.xyz/entity/the-contribution-of-remote-sensing-and-input-feature-selection-for-groundwater-level-prediction-using-lstm-neural-networ (retrieved 2026-05-24)
