农业河流灌溉用水预测模型数据

可以用于农业灌溉用水量预测,输入为农业中地下水的地下水位，降雨量，化学需氧量，硝酸盐，磷酸盐，土壤湿度，土壤类型，肥料用量。输出为灌溉用水量。该模型帮助解决了灌溉用水量和农业气候以及地下水理化指标的关系建模的问题。使用理化实验以及温度计等采集地下水理化指标以及气候因素，将采集的地下水理化指标以及气候数据使用传统算法，多元线性回归算法等方式以预测农业灌溉用水量。该模型通过输入地下水的地下水位，降雨量，化学需氧量，硝酸盐，磷酸盐，土壤湿度，土壤类型，肥料用量，来输出预测的灌溉用水量。

This dataset is designed for agricultural irrigation water consumption prediction. Its input features include groundwater level, rainfall, chemical oxygen demand (COD), nitrate, phosphate, soil moisture, soil type, and fertilizer application rate of agricultural groundwater, with the output being irrigation water consumption. This model addresses the challenge of modeling the correlations between irrigation water consumption, agricultural climatic factors, and groundwater physicochemical indicators. Groundwater physicochemical indicators and climatic factors are collected through physicochemical experiments, thermometers and other measuring instruments. The collected groundwater physicochemical and climatic data are then utilized with traditional algorithms such as multiple linear regression to predict agricultural irrigation water consumption. By inputting the aforementioned indicators including groundwater level, rainfall, COD, nitrate, phosphate, soil moisture, soil type and fertilizer application rate, the model generates the predicted irrigation water consumption.