Scenario Generalization and Sector Transfer in Real Surface Irrigation Surrogate Modelling with Tabular Models

Alessandro Cagiano; M. Aybars Seyhan; Fabiola Gangi; Matteo Zignani; Daniele Masseroni

cs.LGOct 19, 2026

Scenario Generalization and Sector Transfer in Real Surface Irrigation Surrogate Modelling with Tabular Models

Authors: Alessandro Cagiano, M. Aybars Seyhan, Fabiola Gangi, Matteo Zignani, Daniele Masseroni

Organizations: Department of Computer Science, University of Milan, Milan, Italy · Digital Drop s.r.l., Milan, Italy · Department of Agricultural and Environmental Sciences, University of Milan, Milan, Italy

Abstract

Surface irrigation planning requires screening many operating conditions and spatial configurations, while high-fidelity shallow-water-equation (SWE) simulations are expensive. We study tabular surrogate models for predicting maximum water height from drone-derived field morphology, terrain, hydrological, spatial, and scenario descriptors, where each row represents a spatial cell under one irrigation scenario. To assess deployment feasibility, we evaluate gradient-boosted trees, neural tabular models, AutoML, and tabular foundation models (TFMs) under two regimes: same-sector generalization, where a model predicts unseen scenarios after observing a limited set of configurations in the same sector, and sector-out transfer, where knowledge learned from source sectors is transferred to an unseen sector. The main findings are twofold: a) under the evaluated fixed 8/32 same-sector split, surrogates achieve strong performance, especially with raw spatial coordinates; b) sector-out transfer is substantially harder because the target sector has different coordinates and geometry. Sector-frame normalization of spatial coordinates improves transfer across similarly structured sectors, but does not solve cross-shape generalization. Overall, tabular surrogates appear useful for same-sector scenario screening under this fixed split, while robust transfer to unseen sectors remains open.

Figures & tables

Domain	Feature set	Best gradient-boosted	Best neural	Best TFM
Aligned sector A	raw XYZ	0.9461	0.9830	0.9642
Aligned sector B	raw XYZ	0.8892	0.9705	0.9406 †
Aligned sector C	raw XYZ	0.8769	0.9571	0.9319 †
Cross-shape domain ‡	raw XYZ, held-out cells	0.9965	0.9965	0.9978
Aligned sector A	no raw XYZ	0.7615	0.7699	0.7162

Table 1: Same-sector prediction summary. Values are

R^{2}

;

\dagger

marks primary-sample results and

\ddagger

marks a same-scenario held-out-cell split.

Model	Eval. set	Inference time	$R^{2}$
XGBoost	full table	3.6 s	0.9354
LightGBM	full table	99.9 s	0.9461
CatBoost	full table	3.1 s	0.9102
RealMLP-TD	full table	12.2 s	0.9751
TabM-D	full table	61.2 s	0.9830
RealTabPFN-2.5	full table	7203.2 s	0.9642

Table 2: Inference time on aligned sector A. Full-table evaluation uses

\sim

12.7M held-out cells; the primary evaluation uses 512k cells. LightGBM uses GPU training, but prediction is CPU-backed.

Held-out sector	Best gradient-boosted	Best neural	Best TFM	Best overall
Aligned sector A	0.5851	0.5639	0.5885	0.5885
Aligned sector B	0.4625	0.4552	0.4375	0.4625
Aligned sector C	0.6468	0.6093	0.6836	0.6836

Table 3: Sector-out transfer with raw coordinates excluded. Each value is primary-sample

R^{2}

on the held-out aligned sector. Source training uses the remaining aligned sectors with a stratified source-sample budget.

Coordinate protocol	Mean $R^{2}$	$\Delta$
No raw XYZ	0.5319	–
Per-sector min–max XYZ	-0.7010	-1.2329
Sector-frame normalized XYZ	0.7526	+0.2207

Table 4: Aligned-sector coordinate diagnostic using gradient-boosted baselines. Values are mean primary-sample

R^{2}

across XGBoost, LightGBM, and CatBoost with a matched 300k target-stratified training budget. Delta is measured relative to the no-raw-XYZ transfer baseline.

Family	Model	Aligned A	Aligned B	Aligned C	Mean
Neural	RealMLP-TD	0.5312	0.8196	0.7419	0.6976
Neural	TabM-D	0.5730	0.4143	0.7824	0.5899
Neural	TorchMLP	0.6206	0.8011	0.7699	0.7305
TFM	RealTabPFN-2.5	0.5336	0.7733	0.7993	0.7020
TFM	TabICLv2	0.5666	0.7254	0.6893	0.6604
TFM	TabPFN-2.6	0.6651	0.8025	0.8257	0.7644

Table 5: Sector-frame transfer results for neural tabular and TFM models. Values are primary-sample

R^{2}

on the held-out aligned sector; the mean is computed over the three aligned sector-out folds.

Surrogate Modeling

Surrogate modeling builds faster or more interpretable approximations of expensive simulations and black-box models.

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Abstract

Surrogate Modeling

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