Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080)

A Review of Rainfall Prediction Using Machine Learning and Deep Learning

Naushin Sindhi, Aakash Parmar — Tue, 08 Sep 2026 00:00:00 +0000

Exact rainfall forecasting is necessary in disaster management, long-term planning, agriculture, flood control, and water resource planning. In the past decade, there has been rapid development and enhancement in terms of data and computing technologies. The review presents a detailed description of new developments in satellite-based rainfall prediction, hydrological estimation, modelling, and especially changes from traditional methods. Current Artificial Intelligence and Machine Learning systems used in ground-based and satellite datasets consist of popular sources in India. GPM IMERG, Meteorological Department observation data, and CHIRPS datasets support large-scale modelling, but they are still challenged by issues of unequal spatial coverage, time, and variability in climatic regions. It is critical to deal with these problems to develop better prediction models. Machine Learning methods such as Random Forest, Gradient Boosting, Ensemble methods, and Support Vector Machines have been shown to perform well in short-term forecasting of rainfall, primarily because they work with sound input variables and detect hidden regularities. Deep learning models, including LSTM, CNN-based, and hybrid deep network models, also increase predictive ability by modelling detailed, non-linear, and spatiotemporal interdependencies that traditional models do not tend to reflect. This Artificial Intelligence-driven extreme forecasting is particularly beneficial for systems dealing with localised and monsoon rainfall variability.

A Survey of Artificial Intelligence Techniques for Early Leukemia Detection: Advances in Deep Learning, Blood Smear Analysis, and Intelligent Hematology Systems

Varun Doddagoudar, Shashank, Vishwa, Madhushree M — Mon, 31 Aug 2026 00:00:00 +0000

This survey presents a comprehensive review of recent Artificial Intelligence (AI)-based approaches for automated leukemia detection and hematological image analysis. The reviewed studies demonstrate the evolution of automated blood-cell detection and counting using Convolutional Neural Networks (CNNs), followed by hybrid Deep Learning–Machine Learning approaches for leukemia classification, optimized CNN architectures, and portable Raspberry Pi-based diagnostic systems. The survey compares the methodologies, performance, advantages, and limitations of these approaches and identifies important research gaps, including limited datasets, computational complexity, restricted leukemia-subtype coverage, limited clinical validation, and the lack of comprehensive patient-monitoring capabilities. Based on these identified gaps, the survey discusses future directions toward integrated hematology intelligence platforms. The proposed HemaSight AI concept extends the reviewed approaches by considering multiple stages of hematological assessment, including early risk prediction, synthetic visualization, relapse monitoring, and AI-assisted patient communication. Thus, the survey establishes a progression from conventional blood-cell analysis toward intelligent, scalable, and clinically applicable hematology systems.