Ali Raza

Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates

Integration of MRMR algorithm with advanced neural networks for modeling long-term crop water demand in agricultural basins

Improving carbon flux estimation in tea plantation ecosystems: A machine learning ensemble approach

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Evaluating land use and climate change impacts on Ravi river flows using GIS and hydrological modeling approach

Use of gene expression programming to predict reference evapotranspiration in different climatic conditions

Spatio-Temporal Analysis of Vegetation Loss, Land Surface Temperature Changes, and Their Relationship with Vegetation Indices in Zhenjiang, China Using Satellite Imagery

Comparison of Landsat-8 and Sentinel-2 Imagery for Modeling Gross Primary Productivity of Tea Ecosystem

Relation of land surface temperature with different vegetation indices using multi-temporal remote sensing data in Sahiwal region, Pakistan

Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province

Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments

A review of recent advancements in micro combustion techniques to enhance flame stability and fuel residence time

Investigation of Irrigation Water Requirement and Evapotranspiration for Water Resource Management in Southern Punjab, Pakistan

Modelling reference evapotranspiration using principal component analysis and machine learning methods under different climatic environments

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Forecasting Long-Series Daily Reference Evapotranspiration Based on Best Subset Regression and Machine Learning in Egypt

Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data

Evapotranspiration Importance in Water Resources Management Through Cutting-Edge Approaches of Remote Sensing and Machine Learning Algorithms

Monitoring the Dynamic Changes in Vegetation Cover Using Spatio-Temporal Remote Sensing Data from 1984 to 2020

Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

Development of Monthly Reference Evapotranspiration Machine Learning Models and Mapping of Pakistan—A Comparative Study

Misconceptions of Reference and Potential Evapotranspiration: A PRISMA-Guided Comprehensive Review