TY  - JOUR
AU  - Coly, Anabilaye Moussa 
AU  - Ly, Alioune 
AU  - Diouf, Ndolane 
AU  - Tamba, Séni 
AU  - Sakho, Issa 
PY  - 2023
TI  - Research on the Link Between Economic Development Variables and the Rate of Access to Drinking Water in Rural Senegal Using Machine Learning
JF  - American Journal of Applied Sciences
VL  - 20
IS  - 1
DO  - 10.3844/ajassp.2023.65.75
UR  - https://thescipub.com/abstract/ajassp.2023.65.75
AB  - 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.