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Research area

Irrigation Modernization

Modernising irrigation networks with information systems, discharge forecasting, gate automation and the knowledge of the people who run them.

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Irrigation Modernization

Overview

Irrigation performance does not come back with rehabilitation alone. Through SIPASI and our work at DI Sapon in Kulon Progo we bring irrigation operation onto data: forecasting canal discharge, modelling how gate moves shift the water, and capturing what experienced operators know.

Our approach

Every project starts in the field: we sense what is happening, model what it means, and turn the result into decisions farmers and operators can act on.

Why it matters

What changesin the field.

3irrigation areas running SIPASI: Wadaslintang, Mrican and Way Sekampung
0.974R² of the LSTM forecasting primary canal discharge at DI Sapon
0.55–4.16%error of the unsteady-flow gate model against field data

What we do

Our work in irrigation modernization.

SIPASI decision support for operation and maintenance
Deep-learning forecasts of canal discharge
Hydrodynamic modelling and automatic gate control
Knowledge management for irrigation operators

Center projects

What we havebuilt and tested.

2 projects from the center's research portfolio. Open a slide to see the system, the setup and the results.

SIPASI irrigation management information system

A decision support system for irrigation operation and maintenance, built because conventional rehabilitation alone was not restoring irrigation performance.

  • Water availability prediction, crop water requirement and if-then cropping-pattern scenarios
  • Operation and maintenance procedures moved from paper to an information system, with SmartAWS, SmartAWLMS and SmartGateController instrumentation
  • Registered intellectual property since 2021

TeamProf. Sigit Supadmo Arif · Dr. Murtiningrum · Andri Prima Nugroho, Ph.D.

WithDirectorate General of Water Resources, Ministry of Public Works

Irrigation Knowledge Management Center

A platform for sustaining irrigation know-how: online courses, content management, discussion forum, knowledge centre and an AI chatbot.

  • Captures what experienced operators know so it survives staff changes

Student research

Theses thatmoved it forward.

2 undergraduate theses, 1 master's thesis supervised at the center, with their methods, key results and research framework.

  • Knowledge managementIrrigation operation

    Gate operators decide from experience. Quantification Theory I measures how farmers' requests, the previous day's rainfall and the state of the Progo River drive operated discharge at six gates over a season, and turns the pattern into an explicit-knowledge dashboard.

    0.83–0.95correlation between measured and predicted discharge
    0.906R² at Primer Bothokan (MAPE 9.98%)
    • Operators' decisions follow a consistent pattern rather than chance
    • Farmer demand and river condition dominate; accuracy drops on secondary canals with more complex downstream conditions
    • The dashboard supports operators' judgement, it does not replace it

In practice

Irrigation Modernization in practice.

Related publications

  1. 2026SIMULASI SISTEM MONITORING LENGAS TANAH BERBASIS ADC DAN KENDALI IRIGASI OTOMATIS MENGGUNAKAN METODE HYSTERESIS UNTUK APLIKASI PERTANIAN PRESISIJurnal Pertanian Presisi (Journal of Precision Agriculture) · Gunadarma University
  2. 2026Variation of Irrigation Methods and Fertiliser Level in Cultivation of Shallot on Rainfed LandagriTECH · Universitas Gadjah Mada
  3. 2025Design and Implementation of an IoT-Based Irrigation Management System for Alternate Wetting and Drying (AWD) Technique in Rice CultivationJurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) · Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Lampung
  4. 2024Kajian Dampak Penambangan Pasir Terhadap Kualitas Air Sungai Untuk Irigasi di IndonesiaJurnal Teknologi Lingkungan Lahan Basah · Tanjungpura University

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