The Performance of Drought Indices on Maize Production in Northern Nigeria Using Artificial Neural Network Model

Abstract

Drought is widely known to put the ecosystem at risk. It ensues when there is a major rainfall shortage that causes hydrological discrepancies and alters the land productive structures. The degree of rainfall influences the growth and harvests of maize, particularly where irrigation is not practicable. In some parts of northern Nigeria, rainfall is unpredictable and often lower than the quantity needed for a viable crop. For the detection, classification, and control of drought conditions, drought indices are used. There has been notable progress in the last few years in terms of modelling droughts by utilizing statistical or physical models. Despite the successes documented by most of these approaches; a plain, effective, and well-built statistical model is the artificial neural network (ANN). The use of artificial neural networks (ANN) to evaluate the impact of drought indices on maize output in the 17 northern Nigerian states is presented in this research. For a 25-year period from 1993 to 2018, observed annual data of drought indices, RDI, and the Palmer drought indices, which comprise SCPDSI, SCPHDI, and SCWLPM, as well as maize yield (measured in tonnes) in Northern states of Nigeria. The ANN model was evaluated using several activation functions (sigmoid, hyperbolic tangent, and rectified linear unit), hidden layers (1, 2, and 3), and training sets (70%, 80%, and 90%). The Mean Square Error (MSE) was employed to evaluate each ANN model's performance. In summary, most of the states' lowest mean square errors (MSEs) were generated via RELU. Also, as the training percentage increases, the mean square error increases.