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Explainable Artificial Intelligence Tools for Hydro-Climate Modelling
| Name | Role | Institution |
|---|---|---|
| Sadikul Islam | Author, Maintainer (aut, cre) |
ICAR-Indian Institute of Soil and Water Conservation, Dehradun, India |
| Shakir Ali | Author (aut) |
ICAR-Indian Institute of Soil and Water Conservation, Dehradun, India |
| Rajesh Kaushal | Author (aut) |
ICAR-Indian Institute of Soil and Water Conservation, Dehradun, India |
Maintainer: sadikul.islamiasri@gmail.com
xaiHydro provides a unified, hydrology-aware workflow
for applying post-hoc Explainable Artificial Intelligence (XAI) to any
trained hydro-climate predictive model. Three complementary attribution
frameworks are implemented:
| Method | Reference | xaiHydro function |
|---|---|---|
| SHAP | Lundberg & Lee (2017) | hydro_shap() |
| LIME | Ribeiro et al. (2016) | hydro_lime() |
| PDP / ALE | Friedman (2001); Apley & Zhu (2020) | hydro_pdp() |
| Permutation importance | Breiman (2001) | hydro_importance() |
| Breakdown profiles | Biecek & Burzykowski (2021) | hydro_breakdown() |
An internal model registry auto-detects 13 machine-learning frameworks without user-written prediction wrappers. Hydrology-standard metrics NSE, KGE, and PBIAS are computed alongside RMSE and MAE.
Two fully synthetic datasets calibrated to Indian river-basin
conditions enable complete reproducibility without proprietary gauge
records: - sim_streamflow_data() – daily
hydro-meteorological predictors and streamflow response (m3/s) -
sim_drought_data() – monthly predictors and SPEI drought
index
# CRAN (recommended)
install.packages("xaiHydro")library(xaiHydro)
# 1. Generate data
df <- sim_streamflow_data(n = 365, seed = 2026)
y <- df$streamflow
X <- df[, setdiff(names(df), "streamflow")]
# 2. Train any supported model
library(randomForest)
rf <- randomForest(x = X, y = y, ntree = 300)
# 3. Build the explainer (model class auto-detected)
exp <- hydro_explainer(rf, X, y, variable = "streamflow", units = "m3/s")
# 4. SHAP global summary
shv <- hydro_shap(exp, nsim = 50)
plot_shap_summary(shv, top_n = 8)
# 5. LIME local explanation
lr <- hydro_lime(exp, new_obs = X[1, , drop = FALSE])
plot_lime_hydro(lr)
# 6. PDP / ALE profiles
pdp <- hydro_pdp(exp, variable = c("precipitation", "soil_moisture"))
plot_pdp_hydro(pdp)
# 7. Permutation importance
imp <- hydro_importance(exp, B = 20)
plot_importance_hydro(imp)
# 8. Full report panel
hydro_xai_report(exp, nsim = 50, save_path = "xaiHydro_report.png")citation("xaiHydro")Package:
Islam, S., Ali, S., & Kaushal, R. (2026). xaiHydro: Explainable AI Tools for Hydro-Climate Modelling. R package version 0.1.0. https://CRAN.R-project.org/package=xaiHydro
Accompanying book chapter:
Islam, S., Dheeraj, A., Ali, S., Kaushal, R., & Venkatesh, G. (2026). Explainable Artificial Intelligence for Hydro-Climatic Modelling: Methods, Applications, and Implementation Using the xaiHydro R Package. In S. K. Chandniha, A. Mondal, S. Kundu, A. Pandey, & D. Naidu (Eds.), Hydro-Climate Analytics: Remote Sensing, AI and Geospatial Modelling. Springer.
Key review to cite alongside:
Zounemat-Kermani, M., & Kheimi, M. (2026). Explainable artificial intelligence in hydrology: A review. Water Resources Management, 40(3), 106. https://doi.org/10.1007/s11269-025-04435-9
GPL-3 (C) 2026 Sadikul Islam, Shakir Ali & Rajesh Kaushal
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.