- 在Julia中运行 - 在julia中首先加载包,`using HydroTools` - 若没有包加载出错,则安装之,`using Pkg; Pkg.add("HydroTools")`
Scanned 2/12/2026
Install via CLI
openskills install majiayu000/claude-skill-registry---
name: hydro-forecast
description:
---
# 1 运行环境说明
- 在Julia中运行
- 在julia中首先加载包,`using HydroTools`
- 若没有包加载出错,则安装之,`using Pkg; Pkg.add("HydroTools")`
## 说明
先不要立即执行该skills,提醒用户输入的数据的格式。用户需要整理好的数据路径即可。
```
```
`model`模型选择
+ MarrMot
+ XAJ
+ TCN
+ LSTM
+ KAN
如果复杂、参数比较多的模型:要求用户输入模型参数`json`文件。
按照如下示例
```json
{
"clumping_index": 0.62,
"LAI_max_o": 4.5,
"LAI_max_u": 2.4,
"z00": 1.33,
"mass_overstory": 35,
"mass_understory": 10,
"root_depth": 0.6,
"α_canopy_vis": 0.035,
"α_canopy_nir": 0.23,
"r_root_decay": 0.95,
"minimum_stomatal_resistance": 150,
"z_canopy_o": 20,
"z_canopy_u": 3,
"g1_w": 8,
"VCmax25": 62.5,
"leaf_resp_co": 0.0015,
"stem_resp_co": 0.0020,
"root_resp_co": 0.0020,
"fine_root_resp_co": 0.003,
"N_leaf": 4.45,
"slope_Vc": 0.33152
}
```
## 1.1 任务说明
### 1.1.1 `framework`:
```julia
function hydro_forecast(f; model, outdir)
res = ...
fwrite(res.output, ...)
fwrite(res.gof, ...)
fwrite(res.info_flood, ...)
fwrite(res.dat_flood, ...)
fwrite(res.evaluation, ...)
end
function hydro_forecast(X::AbstractArray, Y::AbstractArray; model::Function, outdir = "OUTPUT")
mkpath(outdir)
res; # return a NamedTuple
end
```
**输入**:X, Y, model
**输出**:Qsim, GOF, Pass_rate
+ `output`: 三类数据集的输出,A DataFrame with columns of `date`, `Qsim`,
+ `gof`: 三类数据的拟合优度
+ `info_flood`: 洪水场次信息,`id`, `time_beg`, `time_end`, `duration`, `Q_peak`, `Q_min`
+ `dat_flood`:洪水场次的驱动数据,
+ `evaluation`: 每个洪水场次上的模拟优度, csv
**绘图**:
+ 交给他绘图的函数,数据
**总结**:
+ `evaluation`总结模型预报精度 (`AI执行`)
**内部模块设计**:
+ `flood_division`: 采用R语言,划分洪水场次
+ `划分数据集`:train, test, valid
+ `loss`: 根据拟合优度指标去设计loss,例如KGE, NSE, RMSE,注意loss越小越优。根据loss去优选模型参数。
+ `evaluation`: 在三种数据集,train, test, valid。每个洪水场次的洪峰、峰现时间合格率。
### 1.1.2 `model`:水文模型、LSTM、TCN、KAN
```julia
Ysim = Model(X, Y; params, state) # Lux的设计哲学
```
### 1.1.3 文件保存
文件保存采用Julia包`DataFrames`,`RTableTools`
```julia
using RTableTools
fwrite(df, "out.csv") # df is a DataFrame
```
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