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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44456

Soil Moisture Prediction using Deep Neural Network Approach.

Hiren J. Modha, Ashish M. Kothari

Abstract

Soil moisture content is the most significant element in fit farming output and circulation of water, and its accurate forecast is critical regarding water resource management. Mostly soil moisture is complicated through structural features in addition to climatic complications. It’s tough to come up with an optimal mathematical model for soil moisture since there are so many variables for calculation. Presented forecasting models have issues with prediction accuracy, generality, plus other factors like Prediction performance, as well as multi-feature processing capabilities etc. considering all these factors taking Gallipoli, Turkey as a reference site for developing a deep neural network model to forecast moisture with good accuracy and minimum error. The dataset contains entities since 2008 to 2021. Doing quite a bit of mathematical analysis and establishing the correlation between selected features with the spearman coefficient, the appropriate weather data is able to give proper weight to forecast soil moisture. The output of the proposed method proves that the deep learning approach is realistic as well as efficient for the prediction of moisture. Also, deep learning technique is able to make model generalizations with excellent accuracy and minimum errors which is used to save irrigation water with controlling drought.

Keywords

Deep Neural Network, Machine Learning, Multilayer Layer Perceptron (MLP), Support Vector Machine (SVM), Rectified Linear Activation Function (ReLU).

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