安徽各地区农田土壤水分蒸发量预测数据

通过全自动小型气象站对安徽各地区农田实时监测环境温度、环境湿度、风速、土壤含水率、本周降水量、植被指数、太阳日照时间等数据并每日上传即时数据，根据上述7个数据预测出农田土壤水分蒸发量。该预测数量可为农田水分盈亏分析提供数据支撑，指导农田科学灌溉和排水，从而提高作物的产量和质量。另外可结合地理信息系统（GIS）技术，将各地点的农田地理数据和土壤水分蒸发量信息进行深度整合和分析，绘制地理位置-土壤水分蒸发量地图，以直观的可视化形式呈现给用户，增强地理位置与土壤水分蒸发量关系的理解。每天早上通过全自动小型气象站对安徽各地区不同编号的农田实时监测，采集环境温度、环境湿度、风速、土壤含水率、本周降水量、植被指数、太阳日照时间等数据并每日上传即时数据。 通过广义回归神经网络(GRNN)方法对土壤水分蒸发量进行预测，利用主成分分析法提取影响土壤水分蒸发量的7个因子（环境温度、环境湿度、风速、土壤含水率、本周降水量、植被指数、太阳日照时间），将上述7个因子作为GRNN模型的输入量，土壤水分蒸发量作为输出量从而预测出农田土壤水分蒸发量。GRNN模型预测值与实测值拟合程度较高，模型模拟精度较高，可用于安徽各地区农田土壤水分蒸发量预测。

A fully automated mini weather station is employed to conduct real-time monitoring of environmental temperature, ambient humidity, wind speed, soil moisture content, weekly cumulative precipitation, vegetation index, solar sunshine duration and other relevant data for farmlands across Anhui Province, and upload the real-time data on a daily basis. The predicted farmland soil water evaporation values can provide data support for farmland water surplus and deficit analysis, guide scientific irrigation and drainage of farmlands, thereby enhancing crop yield and quality.

Additionally, by integrating Geographic Information System (GIS) technology, in-depth integration and analysis can be performed on farmland geographic data and soil water evaporation information of each location, and a geographic location-soil water evaporation map can be drawn, presenting the results to users in an intuitive visual format to improve the understanding of the relationship between geographic location and soil water evaporation.

Every morning, the fully automated mini weather station carries out real-time monitoring on farmlands with unique identification numbers across Anhui Province, collecting the aforementioned 7 types of environmental data and uploading the real-time data daily.

Soil water evaporation is predicted using the Generalized Regression Neural Network (GRNN) method. Specifically, Principal Component Analysis (PCA) is utilized to extract the 7 influencing factors (environmental temperature, ambient humidity, wind speed, soil moisture content, weekly cumulative precipitation, vegetation index, solar sunshine duration) that affect soil water evaporation. These 7 factors are taken as the input variables of the GRNN model, while soil water evaporation acts as the output variable, thus achieving the prediction of farmland soil water evaporation.

The predicted values from the GRNN model exhibit a high fitting degree with the measured values, and the model boasts high simulation accuracy, making it applicable for the prediction of farmland soil water evaporation across Anhui Province.

搜集汇总

数据集介绍

特点

安徽各地区农田土壤水分蒸发量预测数据是一个每日更新的企业数据集，包含11539条记录，数据格式为xlsx。该数据集通过监测农田环境参数，利用GRNN模型预测土壤水分蒸发量，支持农田水分盈亏分析，指导科学灌溉和排水，提高作物产量和质量。