# ConvLSTM

Mass-conservation constraints had been embedded only in lumped LSTMs, which yield basin-averaged time series and cannot test whether internal states are hydrologically meaningful. PECAN recasts the constraint in a distributed convolutional LSTM, with a strict precipitation-only variant, PECANMC, and a relaxed variant, PECANMCR, trained solely on streamflow at two gauges. In the snow-dominated North Platte Headwaters in Colorado, both variants match lumped deep-learning baselines and the distributed process model VIC on outlet streamflow and expose spatially explicit internal states, yet diagnostics show the model removes winter snow water storage prematurely instead of holding it for spring release.

Mass-conservation constraints had been embedded only in lumped LSTMs, which yield basin-averaged time series and cannot test whether internal states are hydrologically meaningful. PECAN recasts the constraint in a distributed convolutional LSTM, with a strict precipitation-only variant, PECANMC, and a relaxed variant, PECANMCR, trained solely on streamflow at two gauges. In the snow-dominated North Platte Headwaters in Colorado, both variants match lumped deep-learning baselines and the distributed process model VIC on outlet streamflow and expose spatially explicit internal states, yet diagnostics show the model removes winter snow water storage prematurely instead of holding it for spring release.

Northeast India has lacked a warning system covering landslides, flash floods and road cut-offs together, with monitoring relying on manual reports and fixed rainfall thresholds that rarely reach individual villages. SAMBHV streams IoT telemetry, weather and radar data through one pipeline, uses two-stage hybrid prediction and a three-way vote of physics, AI and ground verification to set alert levels, and runs offline with multilingual query support. Across 311 disaster test cases the platform reports detection F1 of 0.87, an 8% false alarm rate and 30 seconds from threshold trigger to alert.

Northeast India has lacked a warning system covering landslides, flash floods and road cut-offs together, with monitoring relying on manual reports and fixed rainfall thresholds that rarely reach individual villages. SAMBHV streams IoT telemetry, weather and radar data through one pipeline, uses two-stage hybrid prediction and a three-way vote of physics, AI and ground verification to set alert levels, and runs offline with multilingual query support. Across 311 disaster test cases the platform reports detection F1 of 0.87, an 8% false alarm rate and 30 seconds from threshold trigger to alert.

Over Brazil's semi-arid Ceará state, a study using ten Earth Observation variables from 2000 to 2023 measured how much desertification is actually predictable: the state-wide mean degradation index rose about 0.005 per year, yet most year-to-year change at 2.2 km is not explained by these satellite data, and a gradient-boosting model given every channel explained only 4.7% of its variance. A residual ConvLSTM that predicts only the change from the last observed state lowered RMSE against persistence by 1.6% one year ahead and 4.4% four years ahead on a held-out southern band, reproducing 53–69% of the regional drift. The forecast is reliable for the regional trend, not for individual cells.

Over Brazil's semi-arid Ceará state, a study using ten Earth Observation variables from 2000 to 2023 measured how much desertification is actually predictable: the state-wide mean degradation index rose about 0.005 per year, yet most year-to-year change at 2.2 km is not explained by these satellite data, and a gradient-boosting model given every channel explained only 4.7% of its variance. A residual ConvLSTM that predicts only the change from the last observed state lowered RMSE against persistence by 1.6% one year ahead and 4.4% four years ahead on a held-out southern band, reproducing 53–69% of the regional drift. The forecast is reliable for the regional trend, not for individual cells.

This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.

This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.