Harnessing Digital Innovations in Hydrology,
Tackling Environmental Challenges

A hydroinformatics researcher focuses on the use of geospatial data science, AI modeling, and programming to address various hydrological and environmental challenges. With a bachelor's degree in Civil Engineering and a master's degree in Hydroinformatics, combined with skills in programming, I have extensive experience in the fields of hydrology, GIS, remote sensing, data-driven modeling, and numerical modeling.

My research interests are aligned with how digital innovation tools such as GIS, remote sensing, machine learning, and data science can be utilized effectively to address complex environmental challenges, with a focus on hydrology. My research also addresses the pressing challenges of climate change and its impacts on hydrological systems by leveraging these digital innovations.

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Hydroinformatics Research

Hydroinformatics research is an interdisciplinary field that applies computational and data-driven techniques to solve water-related challenges. It combines hydrology, computer science, data science, and engineering to model and simulate water systems, analyze large datasets, and predict behaviors like flooding, water quality, and supply. Key areas include flood and drought risk management, climate change impact assessment, urban water management, and decision support systems for policymakers. The field also focuses on using machine learning, GIS, and remote sensing to optimize water resource management, improve efficiency, and support sustainable development goals, making it crucial for addressing contemporary water issues.

Geospatial Data Science and Analytics

Geospatial Data Science and Analytics is an interdisciplinary field that combines geography, data science, and technology to analyze spatial data and solve location-based problems. It involves collecting, processing, and interpreting data from sources like GIS, remote sensing, and GPS to uncover patterns, trends, and relationships across space and time. Key applications include urban planning, natural resource management, environmental monitoring, transportation optimization, and disaster response. By integrating techniques such as machine learning, spatial statistics, and data visualization, geospatial analytics provides actionable insights for decision-making, enabling sustainable development and addressing complex challenges in various domains.

Most Used tools

  • Python Language

    Python is the primary programming language used for both geospatial data analysis and hydroinformatics research, leveraging libraries such as Pandas, NumPy, Matplotlib, GeoPandas, and GDAL, along with specialized tools like TensorFlow and PyTorch for developing deep learning models to solve hydrological and environmental challenges.

  • GIS Software

    GIS tools like ArcGIS and QGIS are used for mapping, spatial modeling, and data analysis, offering advanced features such as geoprocessing, 3D visualization, and customizable workflows. These tools are also integral to hydroinformatics research, enabling the integration of spatial data with hydrological models to analyze water systems, forecast environmental changes, and support sustainable water resource management.

  • TensorFlow

    TensorFlow is used for developing machine learning models, enabling the analysis of complex spatial patterns and predictive modeling through neural networks and deep learning techniques. In hydroinformatics research, TensorFlow plays a crucial role in modeling hydrological processes, forecasting water levels, and enhancing the accuracy of stormwater and environmental simulations.

  • Hydrological modeling software

    Software such as SWMM, HEC-HMS, and MIKE SHE are used to simulate water flow, rainfall-runoff processes, and drainage systems. These tools support flood forecasting, stormwater management, and integrated surface-groundwater analysis, enabling researchers to design sustainable and effective water resource solutions.

  • Web TOOLS

    Web tools like JavaScript, HTML, CSS, Leaflet, and Mapbox are used to create interactive, responsive maps and web-based geospatial applications. In hydroinformatics research, these tools enable dynamic visualization of hydrological data, real-time monitoring of water systems, and user-friendly interfaces for exploring environmental and stormwater models.

  • Database tools

    Database tools like SQL, PostgreSQL, and other relational and spatial databases are used for efficient data storage, querying, and management. In hydroinformatics research, these tools support the integration and analysis of large hydrological datasets, enable complex geospatial processing workflows, and facilitate the modeling of water systems and environmental processes.

Featured Projects

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

8 Months

Developed a Convolutional LSTM (ConvLSTM) model to forecast water levels in the Everglades wetland in Florida, USA. The model processes raster images of spatiotemporal data for precipitation and water flow, enabling accurate forecasts of water levels in the region up to two days in advance.

Python
TensorFlow
GIS software
Deep Learning
Date Analysis
Remote Sensing

Developing an interactive web map for all the conflicts events in Yemen

2 Months

Developed an interactive web map for the Sanaa Center for Strategic Studies to visualize conflict events in Yemen. The project involved using QGIS for geospatial data preparation and analysis, Python for data processing and cleaning, and Leaflet integrated with JavaScript, HTML, and CSS to design and develop the interactive map. This combination of tools created a user-friendly platform to explore the spatial and temporal patterns of conflict events in Yemen.

Python
QGIS
Leaflet
JavaScript
CSS
HTML
PostgreSQL

Geospatial Data Survey and Analysis for Infrastructure Development-Saudi Arabia

10 Months

Supervised geospatial data collection using advanced surveying tools, such as GPS, ensuring accurate and reliable data acquisition in Neom project in Saudi Arabia. The collected data was then processed using GIS software and AutoDesk Civil 3D to create detailed spatial models and analyses. These processed geospatial datasets were instrumental in the successful design and implementation of large-scale infrastructure projects, including highways, bridges, culverts, and buildings.

ArcGIS
Civil 3D
Python
Data analysis
Data visualization
Survey
Azure

Raidan Bassah

Geospatial Data Scientist and Analyst.