03444nas a2200277 a 450000500170000000800410001703500150005810000200007324501130009326000670020630000340027349000270030750001420033450200590047652021250053565000460266065000530270665000370275970000350279670000540283170000510288571000660293671000400300281000590304285600650310120260818100108.0250820s20259999th u  ms t    000   eng d  a.b124630121 aNakarmi, Rishab10aEnhancing hydrological modeling with explainable AI :ba case study of the Chao Phraya River Basin, Thailand  aPathum Thani, Thailand :bAsian Institute of Technology,c2025  a266 leaves :bill.+e1 online1 aThesis; vno. WM-25-08  aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management  aThesis (M. Eng.) - Asian Institute of Technology, 2025  aHydrological models increasingly adopt deep learning architectures such as Long  Short-Term Memory (LSTM) networks for streamflow forecasting. Despite their high  predictive accuracy, basin-specific implementations often exhibit limited  generalization and seldom incorporate physical catchment characteristics. Addressing  this limitation, this study explores multiple LSTM-based configurations{u2014}both per basin and regional, and with and without catchment attributes.  A key emphasis is placed on the Entity-Aware LSTM (EA-LSTM), an extension of the  standard LSTM that integrates static descriptors through the input gate mechanism,  enabling physically informed generalization across basins. Seven configurations were  evaluated across six sub-basins of the Chao Phraya River (Chao, Pasak, Nan, Ping,  Wang, and Yom), representing diverse hydrological and physiographic conditions. The  regional EA-LSTM, conditioned on attributes such as slope, potential  evapotranspiration, urban area, and erosion, generally outperformed per-basin models,  particularly in structurally complex or data-sparse basins. However, performance  improvements varied across basins, underscoring the interaction between model  architecture and catchment behavior.  To enhance interpretability, SHAP-based feature attribution, embedding analysis, and  input gate bias evaluations were applied. These analyses revealed that the model  captures hydrologically meaningful relationships{u2014}such as the roles of  evapotranspiration, soil texture, erosion, urbanization, etc. in regulating flow. The  learned patterns correspond closely to established physical processes, including runoff  delay and flow regulation, offering insights into the model{u2019}s basin-specific responses.  This work highlights the potential of interpretable regional modeling in hydrology when  catchment descriptors are appropriately integrated. Beyond performance  improvements, the study provides a structured modeling framework and a CAMELS style dataset for Thailand, contributing to the integration of data-driven forecasting with  hydrological realism. 0aHydrologic modelszThailandvCase studies 0aHydrological forecastingzThailandvCase studies 0aDeep learning (Machine learning)1 aShrestha, Sangam,eChairperson0 aNatthachet Tangdamrongsub,eExamination Committee0 aChaklam Silpasuwanchai,eExamination Committee2 aGlobal Water and Sanitation Center (GWSC),eScholarship Donor2 aAIT Scholarship,eScholarship Donor2 aAsian Institute of Technology.tThesis; vno. WM-25-08403Full-Textuhttp://203.159.5.9/ait-thesis/detail.php?q=B22682