Water Saving Irrigation Decision-Making Method based on Big Data Fusion

Abstract

In order to realize the intelligence of irrigation management and the wisdom of irrigation decision-making, improve the efficiency of water resource utilization, and introduce information fusion technology into the field of farmland irrigation, an irrigation decision-making method based on multi-source information fusion is proposed. Firstly, according to the actual situation and specific needs of the study area, the multi-objective irrigation water quantity optimization configuration model is constructed, and the multi-objective intelligent algorithm is used to solve the model. Then, using the adaptive weighted average fusion algorithm, the weight coefficient of soil moisture of millet in different growth stages and different soil layers is constructed, and the fusion of soil moisture in the data layer is realized. Finally, in order to meet different irrigation requirements, the multi-objective particle algorithm is used to solve the multi-object canal optimal water allocation model based on the optimized configuration of irrigation water volume. The experimental results show that the fusion results obtained by the multi-source large data adaptive weighted fusion algorithm are more reasonable, the uncertainty of irrigation decision-making is greatly reduced, the reliability of irrigation decision-making is improved, and the water consumption can be saved by 25.61% by using the multi-objective optimal allocation model.

A Dynamic Big Data Fusion and Knowledge Discovery Approach for Water Resources Based on Granular Computing and Three-Way Decision

“…In which, the particles are basic elements; influence the formation of the grain layer of grain structure. Generally, the particle generation of big data includes two methods: directly generating feature vector; on the basis of feature vector, it is further transformed into data vector representation [19]. Considering that the direct generation of vector representation of text data will lead to the problem of high dimensionality and large sparseness of the data, this paper uses LDA(Latent Dirichlet Allocation) and NMF (Non-negative matrix factorization) to construct particles, and maps the data to the topic-level low-dimensional feature space to generate particles with semantic interpretation [20].…”