Remote sensing, geographic information systems and artificial neural network approach in rainfall estimation the case of the Cagayan river basin, Philippines - Ang, Ma. Rosario Concepcion O. (2011)
Among the hydrologic cycle's elements, rainfall is the most difficult model due to the complexities of the atmospheric processes that generate it and the wide range of spatial and temporal scales where it varies. The amount of rainfall is the major input in a rainfall-runoff model and as such, determines the reliability of water balance equations. Knowledge of rainfall's probability. of occurrence, amount of input, and areal distribution in a catchment is essential in hydrologic modelling. But it has remained a major challenge. Proper rainfall estimation plays a vital role in crop monitoring, drought early warning and flood forecasting.
The Cagayan River Basin, one of the agricultural centers of the Philippines, relies on rainfall in the catchment for crop irrigation, and power generation. However, during the monsoon season, it brings devastating floods ans what typhoon Juan brought in October 2010, and typhoon Pedring and Quiel in October 2011.
Rainfall is measured from different sources such as rain gauge networks, ground-based radar systems and remotely-sensed satellite images. Although conventional rain gauge networks, and radar systems relatively provide direct measurements rain gauges are sparsely located in the Cagayan river basin and provide only point-scale measurements. On the other hand, radar infrastructures are costly and coverage is limited by topography. These limitations of in-situ rainfall observations over wide remote regions make satellite images a promising source of rainfall information to complement ground based measurements.
Several rainfall estimation algorithms that utilizes satellite imageries have been developed. However, these algorithms provide global estimates and are not tailored to characterize the variability of Philippine rainfall. Locally dependent algorithms are necessary for locally focused applications such as at the Cagayan river basin. This research has developed a framework for rainfall estimation in a river basin for proper water resource management and effective flood warning by using remote sensing, geographic information systems and artificial neural networks. Specifically, the study uses MTSAT images together with TMPA rainfall rates and ground-based raingauge measurements.
Two NARX-based (Nonlinear Auto agressive with Exogenous Inputs) neural network models are presented here i.e REIINN (Rainfall Estimation by Information Integration using Neural Networks 1) and REIINN 2. These models were trained using MTSAT cloud-top temperature (CTT)images and rainfall rates from the combined rain gauge and TMPA (TRMM Multi-Satellite Precipitation Analysis) 3B40RT datasets to determine the comples and dynamic realtionsjhip existing between CTT and rainfall rates. REIINN 1 has a parallel architecture, wherein, at a specific time instant, it takes as input the CTT of the four IR channels of MTSAT and its estimated rainfall rate fed back from the previous time instant. REIINN 2 on the other hand, has a series-parallel architecture that takes as input the same CTT images but, with the inclusion of the actual rainfall rate measurements from the previous time instant. The recurrent nature of the two models allows them to account for the temporal dynamics that exist in rainfall patterns. Model performance was assessed using two metrics - root mean square error (RMSE) and correlation coefficient (R). REIINN 1 yielded an RMSE of 8.1423 mm/3h and an overall R of 0.74652 while REIINN 2 yielded an RMSE of 5.2303 and an overall R of 0.90373.These results, especially REIINN 2, are very promising for satellite-based rainfall estimation in a catchment. It is believed that model performance and accuracy will greatly improve with a denser and more spatially distributed in-situ rainfall measurements to calibrate the model on. This research has presented an approach of integrating different information from disparate sources using remote sensing, geographic information systems and artificial neural networks to develop a framework for rainfall estimation.
"October 2011."
Thesis (M.S. Remote Sensing)--University of the Philippines, Diliman.
Keywords: Geographic information systems; Remote sensing; Neural networks (Computer science); Rainfall probability; Rainfall reliability