Help for package xaiHydro

Type:

Package

Title:

Explainable AI Tools for Hydro-Climate Modelling

Version:

0.1.0

Date:

2026-09-09

Description:

Provides a unified workflow for applying Explainable Artificial Intelligence (XAI) methods to hydro-climate predictive models. Functions implement a permutation-based Monte Carlo SHAP estimator (Strumbelj and Kononenko (2014) <doi:10.1007/s10115-013-0679-x>; Lundberg and Lee (2017) <doi:10.48550/arXiv.1705.07874>), a self-contained locally weighted linear surrogate LIME (Ribeiro et al. (2016) <doi:10.1145/2939672.2939778>), and Partial Dependence Plots with Accumulated Local Effects (Friedman (2001) <doi:10.1214/aos/1013203451>; Apley and Zhu (2020) <doi:10.1111/rssb.12377>) with hydrology-specific visualisations and interpretation utilities. Supports any model object compatible with the 'DALEX' explainer interface (Biecek (2018) <doi:10.18637/jss.v097.i01>), including random forests, gradient boosting, and neural networks trained on streamflow, drought indices, flood risk, or evapotranspiration data. Hydrology-standard performance metrics Nash-Sutcliffe Efficiency (NSE, Nash and Sutcliffe (1970) <doi:10.1016/0022-1694(70)90255-6>) and Kling-Gupta Efficiency (KGE, Gupta et al. (2009) <doi:10.1016/j.jhydrol.2009.08.003>) are computed alongside standard regression metrics. Designed to accompany the book chapter: Islam, S., Dheeraj, A., Ali, S., Kaushal, R. and Venkatesh, G. (2026). Explainable Artificial Intelligence for Hydro-Climatic Modelling: Methods, Applications, and Implementation Using the xaiHydro R Package. In Chandniha, S. K. et al. (Eds.), Hydro-Climate Analytics: Remote Sensing, AI and Geospatial Modelling. Springer.

License:

GPL-3

Encoding:

UTF-8

NeedsCompilation:

Depends:

R (≥ 4.1.0)

Imports:

DALEX (≥ 2.4.0), ggplot2 (≥ 3.4.0), dplyr (≥ 1.1.0), tidyr (≥ 1.3.0), patchwork (≥ 1.2.0), cli (≥ 3.6.0), rlang (≥ 1.1.0)

Suggests:

randomForest (≥ 4.7.0), xgboost (≥ 1.7.0), ranger (≥ 0.16.0), testthat (≥ 3.0.0), knitr (≥ 1.45), rmarkdown (≥ 2.25)

VignetteBuilder:

knitr

Config/testthat/edition:

Config/roxygen2/version:

8.1.0

Packaged:

2026-09-13 10:09:33 UTC; Admin

Author:

Sadikul Islam

[aut, cre], Shakir Ali [aut], Rajesh Kaushal [aut]

Maintainer:

Sadikul Islam <sadikul.islamiasri@gmail.com>

Repository:

CRAN

Date/Publication:

2026-09-24 13:30:02 UTC

xaiHydro: Explainable AI Tools for Hydro-Climate Modelling

Description

Provides a unified, hydrology-aware workflow for applying post-hoc Explainable Artificial Intelligence (XAI) methods to any trained hydro-climate predictive model.

Three attribution frameworks are implemented:

SHAP (Lundberg and Lee, 2017): hydro_shap, plot_shap_summary, plot_shap_waterfall
LIME (Ribeiro et al., 2016): hydro_lime, plot_lime_hydro
PDP / ALE (Friedman, 2001; Apley and Zhu, 2020): hydro_pdp, plot_pdp_hydro

Additional tools: hydro_importance, hydro_breakdown, hydro_residuals, hydro_xai_report.

Two built-in fully synthetic datasets support reproducible demonstrations without proprietary gauge records:

sim_streamflow_data – daily hydro-meteorological predictors and streamflow (m3/s), calibrated to Indian conditions.
sim_drought_data – monthly predictors and SPEI drought index.

Authors

Sadikul Islam (author, maintainer), Shakir Ali (author), and Rajesh Kaushal (author). Maintainer contact: sadikul.islamiasri@gmail.com

Citation

To cite the package:

Islam, S., Ali, S., & Kaushal, R. (2026). xaiHydro: Explainable AI Tools for Hydro-Climate Modelling. R package version 0.1.0. doi:10.32614/CRAN.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.

Keywords

explainable AI; SHAP; LIME; partial dependence; accumulated local effects; hydrology; streamflow; drought; SPEI; random forest; XGBoost; model interpretability; Nash-Sutcliffe efficiency; Kling-Gupta efficiency; R

Author(s)

Maintainer: Sadikul Islam sadikul.islamiasri@gmail.com (ORCID)

Authors:

Sadikul Islam sadikul.islamiasri@gmail.com (ORCID)
Shakir Ali
Rajesh Kaushal

References

Apley, D. W., & Zhu, J. (2020). Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society: Series B, 82(4), 1059–1086. doi:10.1111/rssb.12377

Biecek, P. (2018). DALEX: Explainers for complex predictive models in R. Journal of Machine Learning Research, 19(84), 1–5.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. doi:10.1214/aos/1013203451

Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria. Journal of Hydrology, 377(1–2), 80–91. doi:10.1016/j.jhydrol.2009.08.003

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. doi:10.48550/arXiv.1705.07874

Molnar, C., Casalicchio, G., & Bischl, B. (2018). iml: An R package for interpretable machine learning. Journal of Open Source Software, 3(26), 786. doi:10.21105/joss.00786

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines. Transactions of the ASABE, 50(3), 885–900. doi:10.13031/2013.23153

Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I. Journal of Hydrology, 10(3), 282–290. doi:10.1016/0022-1694(70)90255-6

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. doi:10.1145/2939672.2939778

Tyralis, H., Papacharalampous, G., & Langousis, A. (2019). A brief review of random forests for water scientists and practitioners. Water, 11(5), 910. doi:10.3390/w11050910

Zounemat-Kermani, M., & Kheimi, M. (2026). Explainable artificial intelligence in hydrology: A review. Water Resources Management, 40(3), 106. doi:10.1007/s11269-025-04435-9

Prediction breakdown profile for a single observation

Description

Decomposes a single model prediction into additive feature contributions using predict_parts with the SHAP or Break Down algorithm. Shows how each feature pushes the prediction above or below the baseline (mean prediction).

Usage

hydro_breakdown(explainer, new_obs, type = c("shap", "break_down"), B = 25L)

Arguments

explainer

A hydro_explainer object created by hydro_explainer.

new_obs

A single-row data.frame of predictor values.

type

Character. Decomposition method: "shap" (default, averages over random orderings) or "break_down" (greedy, fixed order).

B

Integer. Number of random orderings for SHAP-type breakdown. Default: 25.

Value

An object of class c("hydro_breakdown", "predict_parts").

References

Biecek, P., & Burzykowski, T. (2021). Explanatory Model Analysis. Chapman and Hall/CRC. https://ema.drwhy.ai/

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 120, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  bd  <- hydro_breakdown(exp, X[3, , drop = FALSE])
  plot_breakdown_hydro(bd)
}

Create a hydrology-aware DALEX explainer

Description

Wraps explain with hydrology-specific defaults, variable labelling, and input validation. The returned explainer object is compatible with all xaiHydro analysis functions and can also be passed directly to any DALEX function.

For automatic model-class detection use the trained model directly; all standard S3 predict methods are auto-detected by DALEX.

Usage

hydro_explainer(
  model,
  data,
  y,
  variable = "streamflow",
  units = "",
  label = "hydro_model",
  predict_function = NULL,
  colorize = TRUE,
  ...
)

Arguments

model

A trained model object. Supported interfaces include randomForest, ranger, xgboost, gbm, caret, and any object for which a predict() method exists.

data

A data.frame of predictor variables (no response column).

y

Numeric vector of observed response values, length equal to nrow(data).

variable

Character string naming the hydro-climate target variable. Used for axis labelling. Examples: "streamflow", "drought_index", "evapotranspiration". Default: "streamflow".

units

Character string for the response unit label (e.g. "m3/s", "mm/day"). Default: "".

label

Character string for the model label in plots. Default: "hydro_model".

predict_function

Optional custom predict function with signature function(model, newdata) returning a numeric vector. If NULL, explain auto-detects the method. Default: NULL.

colorize

Logical. Colour-coded console output during explainer creation. Default: TRUE.

...

Additional arguments passed to explain.

Value

An object of class c("hydro_explainer", "explainer") – a list containing the DALEX explainer plus xaiHydro metadata:

hydro_variable: Target variable name string.
hydro_units: Response unit string.
n_obs: Number of observations in data.
n_features: Number of predictor columns.

References

Biecek, P. (2018). DALEX: Explainers for complex predictive models in R. Journal of Machine Learning Research, 19(84), 1–5.

Biecek, P., & Burzykowski, T. (2021). Explanatory Model Analysis. Chapman and Hall/CRC. https://ema.drwhy.ai/

Examples

df     <- sim_streamflow_data(n = 80, seed = 42)
y      <- df$streamflow
X      <- df[, setdiff(names(df), "streamflow")]
lm_mod <- lm(streamflow ~ ., data = df)
exp    <- hydro_explainer(lm_mod, X, y,
                          variable = "streamflow",
                          units    = "m3/s",
                          colorize = FALSE)
print(exp)

Permutation-based variable importance for hydro-climate models

Description

Computes permutation feature importance using model_parts. Each feature's importance is the mean increase in RMSE when that feature's values are randomly shuffled across B repetitions (Breiman, 2001).

Usage

hydro_importance(
  explainer,
  loss_function = DALEX::loss_root_mean_square,
  B = 10L,
  ...
)

Arguments

explainer

A hydro_explainer object.

loss_function

A DALEX loss function object or a custom function(observed, predicted). Default: DALEX::loss_root_mean_square (RMSE).

B

Integer. Permutation repetitions. Default: 10.

...

Additional arguments forwarded to model_parts.

Details

The loss_function argument must be a DALEX loss function object such as DALEX::loss_root_mean_square or a custom function(observed, predicted) returning a scalar numeric. Passing a character string (e.g. "rmse") is not supported in DALEX >= 2.4 and will trigger a warning followed by automatic fallback to RMSE.

Value

An object of class c("hydro_importance", "data.frame").

References

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  imp <- hydro_importance(exp, B = 5L)
  plot_importance_hydro(imp)
}

Local LIME explanation for a single observation

Description

Applies Local Interpretable Model-agnostic Explanations (LIME) to a single observation using a self-contained locally weighted linear surrogate (Ribeiro et al., 2016). A Gaussian radial basis function kernel weights perturbed samples by their proximity to the target observation. The top n_features predictors are selected by coefficient magnitude. No external packages beyond the base xaiHydro dependencies are required.

Usage

hydro_lime(
  explainer,
  new_obs,
  n_features = 6L,
  n_samples = 1000L,
  kernel_width = 0.75,
  seed = 42L
)

Arguments

explainer

A hydro_explainer object created by hydro_explainer.

new_obs

A single-row data.frame of predictor values.

n_features

Integer. Features to retain in the local linear model. Default: 6.

n_samples

Integer. Perturbed samples to generate. Default: 1000.

kernel_width

Numeric. Gaussian kernel bandwidth (in scaled units). Default: 0.75.

seed

Integer random seed. Default: 42.

Value

An object of class c("hydro_lime", "data.frame") with columns feature, effect, feature_value, and r_squared.

References

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. doi:10.1145/2939672.2939778

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  lr  <- hydro_lime(exp, new_obs = X[1, , drop = FALSE])
  plot_lime_hydro(lr)
}

Compute partial dependence or ALE profiles

Description

Computes marginal (PDP, Friedman 2001) or conditional (ALE, Apley and Zhu 2020) profiles for one or more predictor variables using model_profile. ALE is preferred when predictors are correlated (e.g. precipitation and soil moisture in monsoon hydrology).

Usage

hydro_pdp(
  explainer,
  variable = NULL,
  type = c("partial", "accumulated"),
  grid_points = 50L,
  ...
)

Arguments

explainer

A hydro_explainer object.

variable

Character vector. Predictor names to profile. Default: all predictors in the explainer dataset.

type

Character. "partial" (PDP) or "accumulated" (ALE). Default: "partial".

grid_points

Integer. Grid points per variable. Default: 50.

...

Additional arguments forwarded to model_profile.

Value

An object of class c("hydro_pdp", "list").

References

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. doi:10.1214/aos/1013203451

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  pdp <- hydro_pdp(exp, variable = "precipitation", grid_points = 20L)
  plot_pdp_hydro(pdp)
}

Four-panel residual diagnostics for hydro-climate models

Description

Computes model residuals directly from the explainer's stored observations and predict function, then produces a four-panel diagnostic figure: (1) residual histogram, (2) residuals vs predicted, (3) observed vs predicted (1:1 line), (4) residuals over observation index (temporal autocorrelation check).

Usage

hydro_residuals(explainer, title = NULL)

Arguments

explainer

A hydro_explainer object.

title

Character. Panel title. Default: auto-generated.

Value

A patchwork panel of four ggplot2 objects, invisibly. The panel is printed to the active graphics device.

References

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. doi:10.13031/2013.23153

Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I. Journal of Hydrology, 10(3), 282–290. doi:10.1016/0022-1694(70)90255-6

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 120, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  hydro_residuals(exp)
}

Compute SHAP values for a hydro-climate model

Description

Calculates SHapley Additive exPlanations (SHAP) values using a permutation-based Monte Carlo estimator (Strumbelj and Kononenko, 2014). No additional packages beyond the base xaiHydro dependencies are required. Returns a tidy data frame suitable for plot_shap_summary and plot_shap_waterfall.

Usage

hydro_shap(explainer, nsim = 50L, newdata = NULL, seed = 42L)

Arguments

explainer

A hydro_explainer object created by hydro_explainer.

nsim

Integer. Monte Carlo replicates for SHAP estimation. nsim = 50 is adequate for feature ranking; nsim >= 100 recommended for publication figures. Default: 50.

newdata

Optional data.frame of observations to explain. If NULL the background dataset stored in the explainer is used. Default: NULL.

seed

Integer random seed for reproducibility. Default: 42.

Value

An object of class c("hydro_shap", "data.frame") with columns id, feature, shap_value, feature_value, and mean_abs_shap.

References

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. doi:10.48550/arXiv.1705.07874

Strumbelj, E., & Kononenko, I. (2014). Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems, 41(3), 647–665. doi:10.1007/s10115-013-0679-x

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, variable = "streamflow",
                         colorize = FALSE)
  shv <- hydro_shap(exp, nsim = 10, seed = 42)
  head(shv)
}

Generate a full XAI diagnostic report panel

Description

Runs SHAP attribution, permutation importance, and partial dependence profiles and assembles them into a single multi-panel wrap_plots figure. Optionally saves a high-resolution PNG.

Usage

hydro_xai_report(
  explainer,
  nsim = 30L,
  top_n = 6L,
  new_obs = NULL,
  seed = 42L,
  save_path = NULL,
  width = 14,
  height = 10,
  dpi = 300L
)

Arguments

explainer

A hydro_explainer object.

nsim

Integer. SHAP simulation replicates. Default: 30.

top_n

Integer. Top features per panel. Default: 6.

new_obs

Optional single-row data.frame for a breakdown panel. Default: NULL.

seed

Integer random seed. Default: 42.

save_path

Character file path for PNG, or NULL. Default: NULL.

width, height

Numeric. Figure dimensions in inches. Defaults: 14 and 10.

dpi

Integer. PNG resolution. Default: 300.

Value

A patchwork object, invisibly. Always printed.

References

Islam, S., & Kaushal, R. (2026). Explainable Artificial Intelligence for Hydro-Climatic Modelling: Methods, Applications, and Implementation Using the xaiHydro R Package. In S. K. Chandniha et al. (Eds.), Hydro-Climate Analytics: Remote Sensing, AI and Geospatial Modelling. Springer.

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 150, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  hydro_xai_report(exp, nsim = 10, top_n = 4)
}

Plot prediction breakdown for a single observation

Description

Plot prediction breakdown for a single observation

Usage

plot_breakdown_hydro(bd_result, title = NULL, max_features = 8L)

Arguments

bd_result

A hydro_breakdown object from hydro_breakdown.

title

Character. Plot title. Default: auto-generated.

max_features

Integer. Maximum features shown individually; remainder collapsed to "Other". Default: 8.

Value

A ggplot2 object.

References

Biecek, P., & Burzykowski, T. (2021). Explanatory Model Analysis. Chapman and Hall/CRC. https://ema.drwhy.ai/

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 120, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  bd  <- hydro_breakdown(exp, X[3, , drop = FALSE])
  plot_breakdown_hydro(bd)
}

Plot permutation variable importance

Description

Plot permutation variable importance

Usage

plot_importance_hydro(imp_result, top_n = 10L, title = NULL)

Arguments

imp_result

A hydro_importance object from hydro_importance.

top_n

Integer. Features to display. Default: 10.

title

Character. Plot title; auto-generated if NULL.

Value

A ggplot2 object.

References

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  imp <- hydro_importance(exp, B = 5L)
  plot_importance_hydro(imp, top_n = 6L)
}

Plot LIME feature effects for a single observation

Description

Plot LIME feature effects for a single observation

Usage

plot_lime_hydro(lime_result, title = NULL)

Arguments

lime_result

A hydro_lime object from hydro_lime.

title

Character. Plot title; auto-generated if NULL.

Value

A ggplot2 object.

References

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  lr  <- hydro_lime(exp, X[1, , drop = FALSE])
  plot_lime_hydro(lr)
}

Plot PDP or ALE profiles

Description

Plot PDP or ALE profiles

Usage

plot_pdp_hydro(pdp_result, variables = NULL, ncol = 2L, title = NULL)

Arguments

pdp_result

A hydro_pdp object from hydro_pdp.

variables

Character vector. Subset of variables to plot. Default: all computed variables.

ncol

Integer. Number of facet columns. Default: 2.

title

Character. Plot title; auto-generated if NULL.

Value

A ggplot2 object.

References

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. doi:10.1214/aos/1013203451

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  pdp <- hydro_pdp(exp,
                   variable    = c("precipitation", "temperature"),
                   grid_points = 20L)
  plot_pdp_hydro(pdp)
}

SHAP beeswarm summary plot

Description

Produces a beeswarm-style SHAP summary plot showing global feature importance and directionality. Features are ordered by mean |SHAP| (most important at top). Colour encodes the raw feature value (red = high, blue = low).

Usage

plot_shap_summary(
  shap_result,
  top_n = 10L,
  point_size = 1.5,
  alpha = 0.6,
  title = NULL
)

Arguments

shap_result

A hydro_shap object from hydro_shap.

top_n

Integer. Number of top features to display. Default: 10.

point_size

Numeric. Jitter point size. Default: 1.5.

alpha

Numeric. Point transparency (0–1). Default: 0.6.

title

Character. Plot title; auto-generated if NULL.

Value

A ggplot2 object.

References

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. doi:10.1038/s42256-019-0138-9

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  shv <- hydro_shap(exp, nsim = 10)
  plot_shap_summary(shv, top_n = 6)
}

SHAP waterfall plot for a single observation

Description

Shows the additive SHAP contributions for one observation, starting from the global baseline (mean prediction) and accumulating to the final predicted value. Red bars push above baseline; blue bars pull below.

Usage

plot_shap_waterfall(shap_result, obs_id = 1L, top_n = 8L, title = NULL)

Arguments

shap_result

A hydro_shap object from hydro_shap.

obs_id

Integer. Row index of the observation to plot. Default: 1.

top_n

Integer. Features shown individually; remainder collapsed to "Other features". Default: 8.

title

Character. Plot title; auto-generated if NULL.

Value

A ggplot2 object.

References

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. doi:10.48550/arXiv.1705.07874

Examples


if (requireNamespace("randomForest", quietly = TRUE)) {
  df  <- sim_streamflow_data(n = 100, seed = 42)
  y   <- df$streamflow
  X   <- df[, setdiff(names(df), "streamflow")]
  rf  <- randomForest::randomForest(x = X, y = y, ntree = 50)
  exp <- hydro_explainer(rf, X, y, colorize = FALSE)
  shv <- hydro_shap(exp, nsim = 10)
  plot_shap_waterfall(shv, obs_id = 1)
}

Synthetic monthly drought index dataset

Description

Generates a fully synthetic monthly dataset for demonstrating XAI methods on drought prediction models. The response variable is the Standardised Precipitation-Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2010). No real observations are included. Predictor ranges are calibrated to published Indian hydro-climatic summaries (Mishra & Singh, 2010).

Usage

sim_drought_data(n = 240L, seed = 2026L)

Arguments

n

Integer. Number of monthly observations. Default: 240 (20 synthetic years).

seed

Integer random seed. Default: 2026.

Value

A data.frame with n rows and eight columns:

precip_anom: Monthly precipitation anomaly (mm).
temp_anom: Monthly temperature anomaly (degrees C).
pdsi: Palmer Drought Severity Index (dimensionless; Palmer, 1965). Negative = drought.
ndvi_anom: NDVI anomaly (dimensionless). Negative = vegetation stress.
spi_3: SPI at 3-month accumulation (McKee et al., 1993). Negative = below-average precipitation.
spi_12: SPI at 12-month accumulation (McKee et al., 1993).
et_deficit: ET deficit (mm). Always non-negative.
spei: SPEI (Vicente-Serrano et al., 2010). Response variable. Below -1 = moderate drought.

Data status

Fully synthetic. No proprietary or copyrighted records are used.

References

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the Eighth Conference on Applied Climatology, 179–183.

Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1–2), 202–216. doi:10.1016/j.jhydrol.2010.07.012

Palmer, W. C. (1965). Meteorological Drought (Research Paper No. 45). U.S. Department of Commerce, Weather Bureau.

Vicente-Serrano, S. M., Begueria, S., & Lopez-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The Standardised Precipitation Evapotranspiration Index. Journal of Climate, 23(7), 1696–1718. doi:10.1175/2009JCLI2909.1

Examples

df <- sim_drought_data(n = 120, seed = 42)
head(df)
## Distribution of SPEI
hist(df$spei, breaks = 20, xlab = "SPEI",
     main = "Synthetic SPEI distribution")
abline(v = -1, col = "red", lty = 2)  # moderate drought threshold

Synthetic daily streamflow dataset

Description

Generates a fully synthetic dataset of daily hydro-meteorological predictor variables and a corresponding streamflow response calibrated to representative Indian river-basin conditions.

All values are simulated: no real observations are used. The predictor ranges follow published statistics for central India: precipitation, soil moisture, NDVI and evapotranspiration are drawn from ranges typical of gridded climatology and satellite retrievals for the region. The values are illustrative only and are not taken from, nor calibrated against, any specific product or gauge record.

The streamflow response is the non-linear function

Q(t) = 2 + 0.8P + 1.2P_{t-1} + 0.5\bar{P}_{3} - 0.3\text{ET} + 10\,\text{SM} - 0.1\,\text{SWE} + 0.5(\text{NDVI} \times P) + \varepsilon

where all terms are defined in the Value section below.

Usage

sim_streamflow_data(n = 365L, seed = 2026L)

Arguments

n

Integer. Number of daily observations to generate. Default: 365 (one synthetic year).

seed

Integer random seed for reproducibility. Default: 2026.

Value

A data.frame with n rows and nine columns:

precipitation: Daily rainfall (mm). Seasonal mean 5-8 mm/day during monsoon; calibrated to CHIRPS climatology for central India.
temperature: Mean daily air temperature (degrees C). Seasonal cycle centred at 20 degrees C; calibrated to ERA5 and IMD gridded data.
evapotranspiration: Reference ET (mm/day). Analogous to MODIS MOD16.
soil_moisture: Volumetric root-zone moisture (m3/m3, range 0.05-0.50). Analogous to SMAP L3.
snow_water_equiv: Snow water equivalent (mm). Analogous to MODIS MOD10A1.
ndvi: NDVI (dimensionless, 0-1). Analogous to MODIS MOD13A3.
lag1_precip: Previous-day precipitation (mm).
lag3_precip: 3-day moving-mean precipitation (mm).
streamflow: Simulated streamflow (m3/s). Response variable; always positive.

Data status

Fully synthetic. No proprietary or copyrighted observational data are included. Results are illustrative; they should not be extrapolated to specific real catchments without calibration against observed records.

References

Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. doi:10.1016/j.jhydrol.2009.08.003

Zounemat-Kermani, M., & Kheimi, M. (2026). Explainable artificial intelligence in hydrology: A review. Water Resources Management, 40(3), 106. doi:10.1007/s11269-025-04435-9

Examples

df <- sim_streamflow_data(n = 200, seed = 42)
head(df)
summary(df)
## Seasonal streamflow pattern
plot(df$streamflow, type = "l",
     xlab = "Day", ylab = "Streamflow (m3/s)",
     main = "Synthetic daily streamflow")

Package {xaiHydro}

xaiHydro: Explainable AI Tools for Hydro-Climate Modelling

Description

Authors

Citation

Keywords

Author(s)

References

Prediction breakdown profile for a single observation

Description

Usage

Arguments

Value

References

See Also

Examples

Create a hydrology-aware DALEX explainer

Description

Usage

Arguments

Value

References

See Also

Examples

Permutation-based variable importance for hydro-climate models

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Local LIME explanation for a single observation

Description

Usage

Arguments

Value

References

See Also

Examples

Compute partial dependence or ALE profiles

Description

Usage

Arguments

Value

References

See Also

Examples

Four-panel residual diagnostics for hydro-climate models

Description

Usage

Arguments

Value

References

Examples

Compute SHAP values for a hydro-climate model

Description

Usage

Arguments

Value

References

See Also

Examples

Generate a full XAI diagnostic report panel

Description

Usage

Arguments

Value

References

Examples

Plot prediction breakdown for a single observation

Description

Usage

Arguments

Value

References

Examples

Plot permutation variable importance

Description