Forecast of irrigation water demand considering multiple factors

Abstract

Abstract. Many factors influence irrigation water requirement on the basin scale, which make it difficult to obtain comprehensive data. Despite the advantage of less needing historical data, the prediction precision of traditional trend prediction methods is hard to guarantee. For water scarce basins, the artificial influence on irrigation requirement should be thought of as important impact factors. In this paper, the PCA (principal component analysis) method is used to identify the main influencing factors, such as precipitation, irrigation area, water saving technology and so on. Based on that, an irrigation water demand prediction model considering multiple factors is developed for water shortage regions. The method is applied in the Haihe River basin as an example. The study results show that the irrigation water demand forecasting method considering multiple factors in this paper can achieve higher modelling accuracy, compared with the traditional trend prediction method and the method that does not consider the human influence. In view of the small average relative error, 1.32%, it has good values for application.

Irrigation Distribution Network Design Parameters and Their Influence on Sustainability Management

“…Large amounts of information and variables can complicate any model and reduce the precision due to high correlations between its components. Wang et al [158] implemented a regression analysis method to face this problem using Principal Component Analysis (PCA) methods to identify the main factors influencing water demand, keeping as much information as possible while reducing the large amounts of inputs in a multiple-factor irrigation space.…”

“…Without an extended dataset and time requirements, the model can be a powerful tool for developing management strategies. CI.5 [158] Principal Component Analysis (PCA) + Regression Analysis Methods…”

Food sales prediction model using machine learning techniques

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