John et al. (2026) Integrating Geospatial Information and Artificial Intelligence for Irrigation Planning in Kwali Area Council, Federal Capital Territory, Nigeria

Identification

• Journal: Journal of Applied Ecology and Environmental Design
• Year: 2026
• Date: 2026-09-05
• Authors: Audu Matthew John, Nanpon Zitta
• DOI: 10.70382/hujaeed.v13i4.026

Research Groups

• Department of Surveying and Geoinformatics Federal University of Technology Minna, Niger State
• Department of Surveying and Geoinformatics Federal University of Technology Minna, Niger State

Short Summary

This study evaluates irrigation suitability within the Wako Irrigation Site, Kwali Area Council, Federal Capital Territory (FCT), Nigeria, using an integrated GeoAI framework combining geospatial technology and artificial intelligence for precision irrigation planning.

Objective

• To integrate geospatial information and a Multilayer Perceptron Artificial Neural Network (MLP-ANN) for irrigation-suitability assessment and planning in Kwali Area Council, Federal Capital Territory, Nigeria.

Study Configuration

• Spatial Scale: Local scale, focusing on the Wako Irrigation Site within Kwali Area Council, FCT, Nigeria.
• Temporal Scale: The study is based on current environmental conditions and does not account for future climate change projections.

Methodology and Data

• Models used: Multilayer Perceptron Artificial Neural Network (MLP-ANN)
• Data sources:
  • Sentinel-2 Imagery
  • Landsat Imagery
  • Aerial Imagery
  • Survey data
  • Ground truthing

Main Results

• The study area is characterized by nearly level terrain, covering 8.72 km² (58.23%), with gentle and moderate slopes occupying 4.68 km² (31.22%) and 1.58 km² (10.55%), respectively.
• Soil-moisture analysis shows that moist areas constitute the dominant class, covering 6.52 km² (43.52%), followed by wet areas at 4.96 km² (33.13%) and dry areas at 3.50 km² (23.35%).
• The Normalized Difference Vegetation Index (NDVI) values range from 0.031 to 0.473, indicating vegetation conditions ranging from no vegetation to low vegetation.
• Drainage density is the dominant class, occupying 6.62 km² (44.19%), with moderate drainage density covering 4.85 km² (32.38%).
• The integrated GeoAI framework produced irrigation suitability zones categorized into high, moderate, and low suitability.

Contributions

• This study demonstrates the effectiveness of integrating geospatial information and artificial intelligence for precision irrigation planning in Nigeria.
• The developed irrigation-suitability model can support evidence-based irrigation planning, improve water-use efficiency, and promote climate-smart agricultural development in Nigeria.

Funding

• No funding information is provided in the paper.

Citation

@article{John2026Integrating,
  author = {John, Audu Matthew and Zitta, Nanpon},
  title = {Integrating Geospatial Information and Artificial Intelligence for Irrigation Planning in Kwali Area Council, Federal Capital Territory, Nigeria},
  journal = {Journal of Applied Ecology and Environmental Design},
  year = {2026},
  doi = {10.70382/hujaeed.v13i4.026},
  url = {https://doi.org/10.70382/hujaeed.v13i4.026}
}