NEURAL NETWORK MODEL OF SOIL MOISTURE FORECAST NORTH KAZAKHSTAN REGION

Abstract

Dealing with agriculture, it is valuable to know an amount of moisture in a soil and to know how to forecast the stored soil moisture within particular period. Forecasting the stored soil moisture works for planning an extent and structure of crop production areas and adjustment of plant-growing programs. Having known about an amount of moisture in one-meter soil and the depth of precipitation in a vegetation season shall help farmers to determine a seeding time, type of fertilizers depending on soil quality and to work out an irrigation schedule as well. In this regard, over the last few years some vigorous activities applied to machine training methods of the weather forecast have been launched in the world. The goal of present research is to develop an artificial neuron network which shall afford an opportunity to figure out a stored soil moisture prior to outgoing to winter in a short-term. North Kazakhstan Region agrometeorological measuring stations for the period from 2012 to 2022 were used in the course of the neuron network training. The Levenberg-Marquardt algorithm aimed at non-linear regression models optimization was chosen for network training. The algorithm includes sequential approximation of initial parameter values to a local optimum. The mean squared error (MSE) function and the correlation coefficient ensure accuracy and precision of forecasts. As a result, 7 neural networks under MATLAB environment using the Levenberg-Marquardt algorithm, with different input and output data, and with different number of learning iterations came to realization. Following analysis of the results, the choice was fallen on the ANN9 best network offering minimum error function and actual data maximum correlation. The neural network obtained fits for use to make efficient decisions in the North Kazakhstan region agricultural sector in the short term.