Research Article Open Access

Research on the Link Between Economic Development Variables and the Rate of Access to Drinking Water in Rural Senegal Using Machine Learning

Anabilaye Moussa Coly1, Alioune Ly2, Ndolane Diouf3, Séni Tamba4 and Issa Sakho5
  • 1 Department of Science and Technology, Iba Der Thiam University, Thies, Senegal
  • 2 Department of Civil Engineering, Thies Polytechnic School (EPT), Thies, Senegal
  • 3 Department of Physics, Cheikh Anta Diop University, Dakar, Senegal
  • 4 Department of Civil Engineering, Thies Polytechnic School (EPT), Thies, Senegal
  • 5 Development of LMR Sciences, Advanced Technologies and Sustainable, Amadou Mahtar MBOW University of Diamniadio, Diamniadio Urban Hub, Dakar, Senegal

Abstract

After pre-processing a set of data collected from Senegalese state structures, official United Nations (UN), and world bank sites and after replacing missing values by interpolation using the mean between two values, we study the effect of macroeconomic development variables on the rate of access to drinking water in rural Senegal. To do this, we use Machine Learning (ML) techniques such as Linear Regression (LR), Decision Tree (DT), and Random Forest (RF) to identify a hidden correlation between the rate of access to drinking water and other variables. Based on the collected data, LR provides the best predictive accuracy, best RMSE (0.001), best MAE (0.011), and best R2 (96.9%) and help public development net received (ODA_NR_FBC) appears to be the most influential variable, predicting the rate of access to drinking water with greater precision than the other variables. This innovative approach can help us to better understand the factors influencing access to drinking water and to propose effective solutions.

American Journal of Applied Sciences
Volume 20 No. 1, 2023, 65-75

DOI: https://doi.org/10.3844/ajassp.2023.65.75

Submitted On: 4 July 2023 Published On: 20 December 2023

How to Cite: Coly, A. M., Ly, A., Diouf, N., Tamba, S. & Sakho, I. (2023). Research on the Link Between Economic Development Variables and the Rate of Access to Drinking Water in Rural Senegal Using Machine Learning. American Journal of Applied Sciences, 20(1), 65-75. https://doi.org/10.3844/ajassp.2023.65.75

  • 682 Views
  • 459 Downloads
  • 0 Citations

Download

Keywords

  • Data
  • Access to Drinking Water
  • Artificial Intelligence
  • Machine Learning
  • LR
  • DT
  • RF