AI4Water offers a suite of cutting-edge, AI-powered products designed to address critical challenges in agriculture, sustainability, and water management.
Evaluating Climate Change Impact on Agricultural Yield in the Brahmaputra Basin
AI4Water developed an advanced Long Short-Term Memory (LSTM) model to assess the impact of climate change on agricultural yield in the Brahmaputra Basin. By integrating historical climate data, future projections, and agricultural metrics, the model predicts crop yields under various climate scenarios. The study focused on identifying the effects of changing temperature, precipitation patterns, and extreme weather events on rice yield, a staple crop in the region. This AI-driven approach provided actionable insights for farmers and policymakers, enabling the formulation of targeted adaptation and mitigation strategies to enhance agricultural resilience in one of India’s most climate-vulnerable regions
Vietnam – Urban Flood Detection in Ha Noi
The AI4Wayer team conducted a study on AI-based Urban Flood Detection in Hanoi, Vietnam, using machine learning and real-time data to improve flood risk assessment. By integrating satellite imagery, rainfall data, drainage models, and historical flood records, the team developed AI models (CNNs, LSTMs, and Random Forest) for accurate flood prediction. A real-time monitoring system was implemented using IoT sensor data, providing early warnings and insights into urban infrastructure vulnerabilities. This AI-driven approach enhances disaster preparedness, improves flood forecasting accuracy, and offers a scalable solution for other flood-prone cities.
Crop Price volatility under climate scenario in Madhya Pradesh , India
The AI4Wayer team conducted a study on crop price volatility in Madhya Pradesh, India, under future climate scenarios using ARIMAX and LSTM models. By integrating historical crop prices, temperature, precipitation, and extreme weather events, the study analyzed how climate variability influences soybean and wheat price fluctuations. ARIMAX captured short-term trends by incorporating exogenous climate variables, while LSTM, a deep learning approach, effectively modeled long-term dependencies in price patterns. The results highlight significant climate-induced price risks, emphasizing the need for adaptive pricing strategies and climate-resilient agricultural policies. The AI-driven approach provides actionable insights for agribusinesses, policymakers, and farmers to mitigate economic risks.
Crop price volatility, focusing on soybean and brinjal
Researchers and collaborators of AI4Water Ltd. have conducted a study on the impact of meteorological factors on crop price volatility in India, focusing on soybean in Madhya Pradesh and brinjal in Odisha. Using statistical models such as EGARCH and machine learning approaches like SARIMAX and LSTM, the study analyzes the relationship between weather patterns and price fluctuations from 2012 to 2024. The findings highlight how climate variability influences agricultural markets, providing valuable insights for policymakers and farmers to mitigate risks and make informed decisions.