Improved Crop Production Integrating GIS and Genetic Algorithms

1. Improved Crop Production Integrating GIS and Genetic Algorithms Amor V.M. Ines 1 1DoctoralCandidate,Water Engineering and Management, School of Civil Engineering, Asian Institute of Technology, P.O. Box 4 Klong Luang 12120 Pathumthani, Thailand Research Committee: Prof. Ashim Das Gupta (Chairman) Assoc. Prof. Rainer Loof (Co-chairman) Dr. Peter Droogers (Extenal Expert, IWMI) Dr. Roberto Clemente Dr. Kyoshi Honda

2. Introduction • Background of the study • Water and irrigated agriculture • Water saving concepts • Methods: systems integration (RS, GIS, models) • Systems approach with optimization • Evolutionary Algorithms: Genetic Algorithms • Genetic Algorithms in irrigation/agricultural water management are still at its infancy • This study then aims to contribute to the development of GA based methodology for irrigated agriculture

3. Statement of the problem • Limitation of field scale agro-hydrological models: spatial constraint • Regional application of agro-hydrological models: • How to effectively derived the spatially attributed data that are not visible from RS observations for the regional analyses? • How to explore the optimal use of water in crop production considering the system’s spatial attributes ad the availability of resources. • General hypotheses • Systems (regional) and systems analysis • The complexities in the system mentioned above can be accounted for by field scale models through a quasi-regional approach wherein the input data are considered as distributed; • The distributed model parameters can be derived from remote sensing observations though the process of data assimilation by exploring the dependency of the observed hydrological processes to the physical and non-physical properties of the system, an inverse modeling approach;

4. The quasi-regional model can be used to explore options in water management; and • Genetic Algorithms can be used to implement data assimilation and water management optimization. • Objectives • General: • To develop a methodology that could explore improved water management options at the regional level through systems integration. • Specific: • 1. To assess the productivity of the land using simulation models. • 2. To use remote sensing (RS) information as input data to simulation models to quantify systems characteristics. • 3. To develop a Genetic Algorithm (GA) based methodology that could explore improved water management alternatives. • 4. To apply the developed methodology to regional scale application.

5. Scope and limitations • This study confines to the development and application of an integrated methodology to water management in irrigated agriculture by regionalizing a field scale model via the use of advanced spatial information systems (RS and GIS) and an optimization technique based on natural genetics called Genetic Algorithm. • The application and validation of the model are limited only to the tertiary level of a large irrigation system. Only one crop will be considered in the applications, the dominant crop during the growing season under consideration. Wheat (Triticum aestivum) is mostly grown during the rabi season in Northwestern India where the case study was conducted.

6. Parameter Estimation A gap between model development and applications in the field: the availability of accurate input model parameters • Laboratory Methods • Field Methods • Pedo-transfer functions • Inverse modeling • Heuristic approach

7. Methods of Regionalizaton and Data Assimilation Regionalization: • Scaling Approach (similar media scaling) • Aggregated Approach (RS and GIS -> force function/simulation steering) • Stochastic Approach (RS and GIS -> simulation steering) Data Assimilation: • Regional Inverse Modeling (using areal evaporation flux and/or soil moisture)

8. Genetic Algorithms in Water Management • Pipe Network Optimization (Least cost design and rehabilitation) • Groundwater Management (yield optimization,remediation, monitoring and containment) • Parameter Estimation (conceptual rainfall-runoff models, saturated flow models) • Irrigation Planning and Operation (area allocation, furrow irrigation optimization)

9.  A1 B5 B1 Selection Reproduction Crossover Mating Pool A5 B1 B5 Mutation . : GA in a Nutshell Fitness (Measure) variable1 variable2 A1 B1 : (t+1) Population (t) . An Bn   A3

10. Water Management Optimization Model RS/GIS data Water Management Options General Framework of the Study Regional model System characterization DATA Genetic Algorithm

11. System Characterization • Field Scale (Lysimeter study) • GA implementation • Regional Scale (Stochastic parameter estimation technique) • GA implementation

12. Water Management Options Development of WatProdGA model Regional mode SWAP model DATA Genetic Algorithm

13. Upper Limit Feasible region Cave Lower Limit Optimization Model Objective function: Subject to:

14. Crop management Water management Water mgt. Crop mgt. Where: Decision variables and additional constraints:

15. Option1 Option2 Individual Water mgt. Crop mgt.     Unconstrained Form: Penalty Method Penalty function Penalty coefficient

16. STUDY AREA The Study Area After Sakthivadivel et al., 1999 Bhakra Irrigation System, Haryana, India

17. Bata Minor Kaithal Sirsa branch Bata minor (inset)

18. Bata Minor: Zoom In offtake Sirsa branch tail

19. Field Scale Inverse Modeling Daily data: 4-parameter problem

20. ASD: average sum of the difference Daily data: 4-parameter problem

21. 1st layer 2nd layer (a) 1st layer 2nd layer (b) Simulated soil water compared to the base values using ETa as criterion in (a) daily, 4-parameter problem and (b) weekly, 4-parameter problem.

22. 2nd layer 1st layer (a) 1st layer 2nd layer (b) Simulated soil water compared to the base values using ETa as criterion in (a) daily, 8-parameter problem and (b) weekly, 8-parameter problem

23. Scatter diagram of the (a) best: (4-parameter, θ + ETa) and (b) worst: (8-parameter, ETa) observed and simulated soil water contents after a GA solution.

24. SW + ETa SW ETa Simulated and measured soil water using GA derived MVG parameters in the experimental study (4-parameter)

25. SW SW + ETa ETa Simulated and measured soil water using GA derived MVG parameters in the experimental study (8-parameter)

26. Comparison of ETlys and calculated ETpot by SWAP

27. (1) Measured and simulated actual yield (2) Total actual ET and (3) Total drainage during the lysimeter study (4-parameter problem): Experimental case

28. (1) Measured and simulated actual yield (2) Total actual ET and (3) Total drainage during the lysimeter study (8-parameter problem): Experimental case

29. February 4, 2001 March 8, 2001 ETa, mm ETa, mm 2.90 2.48 2.06 1.64 1.22 4.20 0.80 3.44 m m 2.68 1.92 1.16 0.40 Areal ETa Bata Minor: SEBAL Analysis

30. Classification Cropped area Cropped area February 4, 2001 March 8, 2001

31. GA Solution to the Regional Inverse Modeling February 4, 2001 March 8, 2001

32. System Characteristics Derived by GA Crop and Water Management practices (irrigation scheduling, Ta/Tp) Physical characteristics of the system (soil, water quality…)

33. Areal water balance and depth to groundwater from regional inverse modeling.

34. Universe of options Water availability Yieldorwater productivity    Crop management option  Water management option

35. Water Available 500 mm Max. fitness Yield Irrigation How GA Traps the Solution Water Available 200 mm Max. fitness Yield Irrigation

36. GA Solutions

37. Water Management Options Note: A Rainfall of 91 mm was recorded during the simulation period a In terms to Ta/Tp (irrigation scheduling criterion) b In terms of emergence dates

38. WatProdGA optimum solutions to the water management problem

39. Optimized distribution of irrigation, yield, PWirrigation, PWprocess and PWprocessdepletion when the average water supply is around 300 mm.

40. Optimized distribution of irrigation, yield, PWirrigation, PWprocess and PWprocessdepletion when the average water supply is around 500 mm.

41. Conclusions This research was conducted to develop a methodology that can be used to explore improved water management options in irrigated agriculture. This was achieved by developing a deterministic-stochastic agro-hydrological model that can simulate regional hydrological process and crop growth. The quasi-regional model was found to be robust and can define the areal water balance and crop growth at every stage of the growing season. One limitation however is the effect of overland flow between homogenous soil units but this could be minimal in irrigated areas in arid or semi-arid regions whose parcels of land are separated by bunds. The final outcome was a decision support model called WatProdGA, which is an implementation of GA optimization using the quasi-regional model. However, it is well known that the availability of accurate input parameters creates a gap between model development and application in the field. Inverse modeling can bridge the gap sufficiently. In this regard, GA was found to be a powerful tool in inverse modeling.

42. The RS-combined modeling approach in parameter estimation on regional basis is a promising approach in hydrological studies. The stochastic parameter estimation technique developed here was applied successfully to characterize the system under investigation. The spatial and temporal data from RS/GIS have improved significantly the regional applications of field scale agro-hydrological models. In general, GA based decision support models are found to be robust. Based on the application of the WatProdGA model, the following conclusions can be drawn: 1)  When water is scarce, equitable water distribution increases the overall performance of the system. 2)   There is an optimal water distribution (volume and timing) to achieve the best possible yield; beyond this level of supply water can be saved for other purpose. 3)  Water and crop management practices should be synchronized to achieve the best possible outcome from the system.

43. Recommendations 1) A comparison of GA with other global optimization methods like Ant Colony Optimization (ACO) and the Sequential Uncertainty Domain Parameter Fitting (SUFI) in inverse modeling.  2) The inclusion of the sensitive parameter(s) on ET estimation as unknown parameter in inverse modeling. 3) A comparison of the stochastic with the aggregated approach in regional inverse modeling.  4) An economic optimization using WatProdGA model to account the constraints on labor, and environmental requirements.  5) The implementation of the methodology on seasonal, multi-crop situation.  6) The implementation on inter-seasonal, multi-crop condition.  7) The implementation of the methodology at higher level of the canal hierarchy.  8) Verification of the overall impact of a rotational water distribution system at the minor level.

44. Thank you very much…

45. RESOURCES AVAILABLE We can explore options in water management Systems and Systems Analysis EXTERNAL STIMULI field level Physical properties (soil, water quality, GW depth…) Management practices (water, crop mgt…) Capable of Regional scale application GA can help us breed an option or combination of options for best possible results To characterize this complexity in the system System We need a ROBUST model

46. Lysimeter

47. System Characterization [x, y] Irrigation dates, depths Spatial distribution yield t+2t t+t ETa water balance Extended SWAP SEBAL By Genetic Algorithm water productivity . . . t t+2t … t+t t+nt Past Time The future

48. Field Scale Inverse Modeling

49. Regional Scale Inverse Modeling

50. Results of Regional GA Implementation