本文選題：智能化灌溉 + PSO-BP神經(jīng)網(wǎng)絡(luò)��； 參考：《西安建筑科技大學(xué)》2014年碩士論文

【摘要】：水是維持人類生存和社會(huì)發(fā)展的基本要素。我國(guó)水資源短缺問(wèn)題日益嚴(yán)重。隨著我國(guó)城鎮(zhèn)化進(jìn)程的加快，城市綠地面積迅速增加，綠地灌溉需水量也大幅度增加，加之水污染等環(huán)境問(wèn)題的威脅，致使城市供水問(wèn)題形勢(shì)更加嚴(yán)峻。提高水資源利用率，走節(jié)水、省水路線，是解決我國(guó)城鎮(zhèn)供水緊張問(wèn)題的根本途徑。城鎮(zhèn)綠地灌溉勢(shì)必以精量化、智能化節(jié)水灌溉模式取代當(dāng)前的粗放式灌溉模式。 本文首先建立PSO-BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)參考作物蒸騰量ET0，在此基礎(chǔ)上建立灌水系數(shù)模糊推理系統(tǒng)以及灌溉模糊決策系統(tǒng)，最終實(shí)現(xiàn)對(duì)綠地適時(shí)、適量地灌溉。 本文選用9組不同氣象因子組合作為輸入量，建立BP神經(jīng)網(wǎng)絡(luò)，通過(guò)理論分析以及大量重復(fù)實(shí)驗(yàn)，確定網(wǎng)絡(luò)結(jié)構(gòu)。然后采用PSO算法優(yōu)化BP網(wǎng)絡(luò)的權(quán)值和閾值，建立PSO-BP神經(jīng)網(wǎng)絡(luò)。利用訓(xùn)練好的PSO-BP模型預(yù)測(cè)參考作物蒸騰量。將不同輸入得到的預(yù)測(cè)結(jié)果與PM ET0對(duì)比分析，結(jié)果表明，只需選用平均溫度、平均相對(duì)濕度、風(fēng)速和日照時(shí)數(shù)等四項(xiàng)影響因子作為輸入，即可通過(guò)PSO-BP模型得到較為精確的參考作物蒸騰量預(yù)測(cè)值，為氣象資料缺失情況下準(zhǔn)確預(yù)測(cè)參考作物蒸騰量提供了一個(gè)可行的方法。 本文利用經(jīng)模糊處理的綠地作物系數(shù)估算綠地作物實(shí)際蒸騰量。以實(shí)際蒸騰量和土壤有效含水量為輸入，，灌水系數(shù)為輸出，建立灌水系數(shù)模糊推理系統(tǒng)。在此基礎(chǔ)上結(jié)合灌水定額公式及水量平衡方程等，建立灌溉模糊決策系統(tǒng)，由此系統(tǒng)決策灌水量和灌水時(shí)間。用2005年的相關(guān)數(shù)據(jù)對(duì)灌溉模糊決策系統(tǒng)進(jìn)行測(cè)試，結(jié)果表明，利用本系統(tǒng)進(jìn)行灌溉決策，可使土壤有效含水量較為穩(wěn)定的保持在適宜作物生長(zhǎng)的范圍內(nèi)。在保證作物健康生長(zhǎng)的前提下最大限度的節(jié)約了水資源，對(duì)節(jié)水灌溉的廣泛實(shí)施具有一定的指導(dǎo)意義。
[Abstract]:Water is the basic element to maintain human survival and social development. The shortage of water resources in China is becoming more and more serious. With the acceleration of urbanization in our country, the area of urban green space is increasing rapidly, the irrigation water demand of green land is also increasing by a large margin, and the threat of environmental problems, such as water pollution, makes the situation of urban water supply more serious. To improve the utilization rate of water resources, to save water and to save water is the fundamental way to solve the shortage of water supply in cities and towns in China. Urban greenbelt irrigation is bound to be quantified, intelligent water-saving irrigation mode will replace the current extensive irrigation mode. In this paper, the PSO-BP neural network is established to predict the reference crop transpiration et _ 0. Based on this, a fuzzy inference system for irrigation coefficient and a fuzzy decision-making system for irrigation are established. In this paper, 9 groups of different combinations of meteorological factors are selected as inputs, and BP neural network is established. The network structure is determined by theoretical analysis and a large number of repeated experiments. Then the weight and threshold of BP neural network are optimized by PSO algorithm, and the PSO-BP neural network is established. The reference crop transpiration was predicted by the trained PSO-BP model. The predicted results obtained from different inputs are compared with PM ET0. The results show that only four factors, such as mean temperature, mean relative humidity, wind speed and sunshine duration, are selected as input. A more accurate prediction value of reference crop transpiration can be obtained by PSO-BP model, which provides a feasible method for predicting reference crop transpiration in the absence of meteorological data. In this paper, the actual transpiration of green land crop is estimated by fuzzy processing. The fuzzy inference system of irrigation coefficient was established by taking actual transpiration and soil effective water content as input and irrigation coefficient as output. On the basis of this, the irrigation fuzzy decision system is established by combining the irrigation quota formula and the water balance equation, so that the irrigation quantity and irrigation time can be determined by the system. The fuzzy decision system of irrigation was tested with the relevant data in 2005. The results showed that the effective water content of soil could be kept within the suitable range of crop growth by using the system to make irrigation decision. On the premise of ensuring the healthy growth of crops, the water resources are saved to a great extent, which has certain guiding significance for the wide implementation of water-saving irrigation.
【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU985.14

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